Big Bang Theory And Christianity Religion Essay

The highly heated debate and discussion about how our universe and earth began has been going on for many centuries. In the old days many people believed that God created the universe. They even believed that the planets and the sun revolved around the earth. With today’s technology scientists scientist have been able to explore our outer world. In their research some scientists scientist have begun to believe that the universe was created not by a God or a divine force but by a big explosion called the Big Bang Theory.

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About 15 billion years ago an explosion started the expansion of the universe. Scientists refer to the explosion as the Big Bang. What happened in this event was all the matter and energy of space contained at one point. The explosion is not an explosion of a bomb where particles are thrown outward but it consisted of an explosion of space within itself. All the galaxies and planets were not clumped together, but the explosion or Big Bang laid out the foundations for the universe. (The Big Bang)
How the Big Bang theory came to existence was from the observations of Edwin Hubble. He observed that the universe is always expanding. He calculated that a galaxy’s velocity is proportional to its distance. This means if galaxies are twice as far from us they move twice as fast. He also found that the universe is expanding in every direction. With these observations Hubble concluded that it has taken every galaxy the same amount of time to move from a common starting position to its current position. As galaxies kept moving farther away from each other it is called the red shift. As light reaches earth from other galaxies there is a greater distance between earth and the galaxy, which leads to wavelengths being stretched. (The Big Bang)
In 1964, Arno Penzias and Robert Wilson were two astronomers who discovered cosmic microwave radiation. The two men discovered this when one day they were trying to detect microwaves from outer space, but accidentally they discovered a noise of extraterrestrial or alien origin. The noise came from all directions and not just one direction or location. What they heard became obvious to them what they discovered was radiation from the farthest points of the universe. By discovering these microwaves they could calculate what Edwin Hubble calculated also. They concluded that everything in the universe came from the same point, which they say was the explosion which is referenced as the Big Bang. Everything in the universe moves away from each other at the same rate and velocity. NASA has sent a satellite and has been able to detect cosmic microwaves from the outer reaches of the universe proving the discovery of Arno Penzias and Robert Wilson. (The Big Bang) (Origins of the Universe)
Edwin Hubble’s discovery of finding that the universe is always expanding and everything is moving away from each other at the same speed was the base of the Big Bang Theory. Then when Arno Penzias and Robert Wilson discovered cosmic microwaves radiation is just supported more evidence of the Theory Edwin Hubble created.
The Big Bang Theory is the origin of our universe which means it is the theory of how our universe was created. When the universe was created billions of years ago, billions of years after the planet Earth was created. Thousands of rocks were orbiting the sun and many of them started to crash into each other. The collisions of rock created mass amounts of energy and formed Earth. Earth took its form when the initial lava mass cooled and it formed the outer crust of our Earth. But with the outer crust covering all the heat and energy of the earths core, the middle of the earth is as hot as the sun. (Earth, Earth Science, Planet Earth)
Earth’s distance from the Sun allows the ability to absorb solar energy and create photosynthesis. When photosynthesis occurs it lets off oxygen and this began the accumulation and storage of oxygen in the Earth’s atmosphere. With all the build up in the atmosphere it lead to the development of what is now called the ozone layer. With the ozone layer around Earth, it blocks out all the harmful ultraviolet radiation waves from the Sun. With the ozone protecting the Earth’s surface, multicellular organisms slowly became populated to the planet Earth. (Earth, Earth Science, Planet Earth)
The explosion known as the Big Bang started the origin of the universe, which created the universe, earth and eventually human beings. Without the great observations and calculations of Edwin Hubble humans would not know how the universe might have begun. With his discoveries he developed the Big Bang Theory which was the origin of the universe. Edwin also got some back up when Arno Penzias and Robert Wilson discovered cosmic microwaves radiation, which helped support Hubble’s thought of everything in the universe starting at the same point.
The Big Bang theory has some evidence that might make the theory true but it is still a theory, and it cannot be proven. Many people may believe that there was a Big Bang that started our universe and our earth, but there are also billions of Christians and other religions in the world that believe in their own God and that he created the universe.
Christians are monotheists which means they believe in one God. Some Christians believe that the Big Bang theory is totally false and that God their creator made all the planets, stars, our earth and the people who live on the earth. But, other Christians believe that God did create everything, but they also believe he could have made the universe any way he wanted to. He could have done it like it says in the Bible in the first two chapters of Genesis. In chapters one and two of Genesis it describes how God made light and dark and all the planets, stars, Heaven and earth, or God could have created everything with an explosion. But those Christians believe that God still created all the particles in the explosion and he controlled the explosion and it was all a part of his greater plan which was to create man and woman in his image.
So, many Christians believe that there was no Big Bang or explosion, but rather that everything was created by a higher force which is God. But, there are also some Christians who see and believe some of the evidence Hubble and Arno Penzias and Robert Wilson found, but they challenge the Atheists and ask them where did the energies and powers come from. The Christians who believe in God and the Big Bang theory say that the powers and energies in the explosion came from God and was the entire plan God created to make our universe, our planets, earth and moon.
Although there are billions of Christians in the world who believe God is the creator of everything, there are also many Atheists who don’t believe in God. Atheists are people (many of them are scientists ) who believe that everything was all started and can be explained by science. Atheists believe that the Big Bang theory was a likely origin of our universe.
Atheists believe that science is the answer to everything and that science can explain everything. They believe that there is no higher power or God. They believe that there is no way there is a God. Atheists believe that you have to see it to believe it. They believe in science because science is something they can see and they can prove everything by science. But still many things in science are theories and have not been fully proven.
When Atheists are challenged by Christians they are often asked where did the energies and the particles come from? Did they just appear? Atheists come back with the counter question of where did the Christians creator God come from? Did he just appear? That is the mystery of life and the Atheists believe that everything began with science and everything can be explained through science, which is why Atheists believe in the Big Bang Theory. On the other side of the argument , the Christians believe in a God and that this is a higher power that created the universe, planets, moons, stars, and humans who live in this massive universe.
“THE BIG BANG.” University of Michigan. Web. 11 Dec. 2010. .
“Origins of the Universe.” National Geographic. Web. 11 Dec. 2010. .
“Earth, Earth Science, Planet Earth at SPACE.com.” Learn More at Space.com. From Satellites to Stars, NASA Information, Astronomy, the Sun and the Planets, We Have Your Information Here. Web. 11 Dec. 2010. .
 

Analyzing of Economic Data Using Big Data

N.Rajanikumar, Dr.A.Suresh babu, Mr.G.Murali

 
Abstract: Big data can help at the e commerce data. The big-picture problems, the economic indicator many investors, business fortunate and judges are rely on are just too outdated by the time they’re out. People “pitch to the number,” but the world has often moved since it was considered and they won’t know it until the next report comes out. Take, for example, the case of increasing food prices in India and China that are pouring up price rises for a major percentage of the world’s residents. But principle claims to have been seeing the movement shaping up for weeks. Premise is able to capture economic data in close to real-time in some cases or at least much closer to it in others thanks to the technology trifecta of e-commerce, cloud computing and Smartphone’s. However, while e-commerce data is supportive for gauging the prices of certain goods in certain economies, it doesn’t really touch emerging economies where the vast popular of transactions are still local and cash-based. If groceries prices are rising across Asia, for example, that likely income, along with other things, inferior health and less money to spend on non-essential end user goods. That’s where mobile devices come into play in the form of Premise’s Android host. The company has more than 700 contributors in 25 cities, mostly in Asia and Latin America, who go into stores and markets and capture data about exact items on which Premise desires data. “We use them as sort of detection agents. The contributors take a picture of the item either on the shelf or in a market stall; it syncs with Premise’s servers in the blur; and Premise’s system is then able to extract information from the photos. It can verify information such as price, brand and quality of the items, and even ecological information such as how clean the store is and how stocked the shelves are. Interestingly, but not without warning, the app that contributors use is only for Android phones.
Keywords: Apache Hadoop API Using HDFS, Mapreduce, Pig, Hive, Linux-Unix, windows,Eclips.
1. INTRODUCTION
This paper mainly focuses on how to manage huge amount of data and how to analyse the data. The technology used for this is hadoop technology . In this project the data taken is Economic data from various E-commerce websites. Then the data is stored into HDFS( hadoop distributed file system) format in the form of clusters. After the storage is done, then the processing of data can be done based on the user requirements. The processing of data can be done using many modes. Hadoop basically contains many ecosystems which provide different ways of processing or analyzing the data in different environments. There are two basic methods of Hadoop are HDFS and MapReduce. HDFS is used to stock up the data and MapReduce is used to progression the data. In MapReduce we write codes in java to analyze the data in whatever way we want to. The ecosystems in Hadoop are also for processing and analyzing the data. The different ecosystems of hadoop are pig, hive, chukwa, HBase, ZooKeeper, sqoop etc. Here pig, hive and sqoop have been implemented. So the first ecosystem implemented is pig. Pig is scripting language. It can process both structured and unstructured data. In this pig scripts are written on the data to get results. Then hive is a query language, it can handle only structured data. In this queries are written on to the data to analyze it. Then finally sqoop, it is actually a support for hadoop rather than an ecosystem. It is used to transfer data from one data base to other. And after the processing of data the results are displayed.
2. What Is Big Data?
Big Data refers to the data sets whose size makes it difficult for commonly used data capturing software tools to interpret, manage, and process them within a reasonable time frame. Big data sizes are a continually moving target, as of 2012 ranging from a few dozen TERABYTES to many PETABYTES of data in a single data set. With this difficulty, new platforms of “big data” tools are being developed to handle various aspects of big quantities of data.
BIG DATA concept means a datasets which continues to grow so much it difficult to manage it using existing database management concept and tools. The difficulty can be related to retrieve the capturing of data, storage, searching and virtualization, etc.
The challenges associated with Big Data are the “4 V’s”:
Volume, velocity, Variety, and value.
The Volume challenges exist because most businesses generate much more then what their system were designed to handle.
The velocity challenge exists if company’s data analysis or data storage runs slower than its data generation.
The variety challenge exists because of the need to process difference types of data to produce the desired insights.
The value challenge applies to deriving valuable insights from data, which is the most important of all V’s in my view.

Fig1. 4V’s of Big Data
3. What is E-Commerce?
A type of trade model, or part of a larger business model, that enables a firm or individual to perform business over an electronic network, typically the internet. Electronic commerce operates in all four of the major market segments: business to business, business to consumer, consumer to consumer and consumer to business. It can be thought of as a more advanced form of mail-order purchasing through a catalogue. Almost any product or service can be offered via ecommerce, from books and music to financial services and plane tickets. Investopedia explains ‘Electronic Commerce: e-commerce’
E-commerce has approved firms to set up a market existence, or to improve an active market spot, by providing a cheaper and more capable distribution chain for their products or services.
4. Why Big Data is a must in ecommerce
The buzz nearby Big Data is far away from being needless. Not only does it permit merchants to gain deeper insights into customer behavior and industry trends, but it also lets them make more precise decisions to improve just about every feature of the business, from selling and publicity, to merchandising, operations, and even customer maintenance.
Below are a few more points that deeper explain the impacts of Big data in the Ecommerce empire. From improving customer familiarity to developing better products or marketing campaigns, it’s no question that Big Data is the next big thing for online businesses.
5. Characteristics of Big Data
A Big data proposal can give a solution which is planned specifically with the needs of the venture.
The following are the basic characters of the Big data:

Comprehensive – It should offer a broad platform, and address all three dimensions velocity, volume and variety.
Enterprise Ready – It should include the performance, reliability, performance and security features.
Integrated – It should enable integration with information supply chain including databases, data warehouses and business intelligence applications.
Open Source Based — It should be open source technology with enterprise class functionality.
Low latency.
Robust and reliability.
Scalability.
Extensibility.
Allows adhoc queries.
Minimal Maintenance.

6. BIG DATA OFFERS
There are many vendors offering BIG DATA Analytics are IBM, KOGNITO, etc. Here in this paper I have discussed about the IBM Platform.

Fig -2: IBM Platform of BIG DATA
7. Big Data Challenges
There are focal challenges of BIG DATA are data variety, velocity, volume and analytical workload intricacy
More number of organizations is belligerent to compact with many problems with the large amount of data. In order to solve this problem, the organizations need to ease the amount of data being stored and develop new storage techniques which can improve storage use.
8. Uses of Big Data for Online Retailers
Most minute merchants’ think that Big Data analysis is for well-built companies. In fact, it is essential for minute businesses, too, as they attempt to partake with the larger ones. This becomes even more important as online retailers proceed together with their customers in real time. Note, however, that management large sets of data can increase a site’s load time. A slow site troubles every aspect of the shopping procedure.
Here are six uses of Big Data for online retailers.
Personalization, Dynamic pricing, Customer service, Managing fraud, Supply chain visibility,Predictive analytics.
‘Big Data’ and e-commerce

Tuesday 25 September 2012
9. Conclusion
The expansion of information particularly of unstructured dataposes a special challengeas the volumeand diversity ofdata. One of the most promise technologies is the Apache Hadoop and Map Reduce structure for dealing with this big data problem.
Big Data is a popular trend in business and in marketing. The concept can indicate different things to different businesses. For ecommerce, retailers should seek to use Big Data to collect big information, if you will, that may be used to make better marketing decisions,.
10. REFERENCES
[1] Ecommerce.about.com
[2] bloomreach.com/2012/05/ecommerce-challenges-that-can-be-solved-by-hadoop-and-big-data-apps/
[2] Ziff Davis, “E-Commerce.” Software World, 2003, vol. 30,pp. 207-212.
[3] X. J. Tong, W. Jiang, “Research of Secure System of Electronic Commerce Based on Mix Encryption,” Microprocessors, 2006, vol. 4, pp. 44-47.
[4] S. H. Qing, Cryptography and Computer Network Security. Beijing: Tsinghua University Press, 2001.
[5] Y. P. Hu, Y. Q. Zhang, Symmetric Cryptography. Beijing: Machinery Industry Press, 2002.
[6] S. Z. Guan. Public Key Infrastructure PKI and Certification Authority. Beijing: Publishing House of Electronics Industry, 2002.
 

Healthcare Technology and Big Data

Introduction
As technology advances, medical devices are able to record increasing amounts of information. These devices are also becoming much more assessable to consumers than in the past. In Adam Tanner’s article “Health Entrepreneur Debates Going To Data’s Dark Side,” he discusses the company Safe Heart. Safe Heart is developing medical devices for consumer use. These devices are able to measure values like blood oxygen saturation, heart rate, and perfusion index. Being able to collect these massive amounts of data, places these devices in the realm of big data. Although the topic of big data imposes its own issues, the medical nature of the data creates an additional set of important issues.

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Safe Heart is not the first organization to develop devices that collect “big” quantities of data. In recent years, many organizations have begun to capture and use large quantities of medical data. Hospitals, credit agencies and researchers have all started to use medical data to the advantage of either the patient or their own corporation. With all the data being captured, there are legal and ethical issues that become apparent.
Main Issues
The most prominent issue related to big healthcare technology data is a legal one. The Health Insurance Portability and Accountability Act (HIPAA), protects health data that is transmitted by a certain groups and organizations [1]. It states that consent must be obtained from the patient to distribute any information to a third party. The organizations included are health plans, health care clearinghouses, and some health care providers [1]. This would mean that non-health organizations transmitting health information would not be subject to HIPAA. The previously mentioned organization Safe Heart, would not be subject to HIPAA because they are not an organization covered by the act. Safe Heart would be able to transmit data in a variety of ways and not be limited by the restrictions of HIPAA. Another act that has the power to govern patient data, but is not optimized for current technologies, is the Privacy Act [1]. The Privacy Act protects data that is distributed by the federal government. To distribute data, the government must remove personally identifying information from the records [1]. After the information is removed, this allows the government to distribute massive amounts of civilian health data publically. As long as explicitly identifying attributes like name and address have been removed, the Privacy Act does not limit how much, or where the data can be distributed. There are few bounds on what the government can do, making this a pressing legal issue.
Big data also imposes several ethical issues on healthcare technology. Even though health agencies may anonymize data in accordance with the Privacy Act, it is still possible to associate the data back to the individual. The Massachusetts Group Insurance Commission released a dataset in the 1990s, and they assured the public that the data had been completely anonymized. A graduate student at the time combined this dataset with voting data and was able to associate medical data back to the correct patient. Shortly after this, it was shown that an American can be identified with only their zip code, birthdate and sex [2]. This imposes a myriad of issues on medical technology companies like Safe Heart. If a released dataset is not properly anonymized, the large amounts of data collected by the devices can be associated back to the patients. This also has powerful ethical implications when considering the results of a study done by the Privacy Rights Clearinghouse. This organization studied a collection of mobile health and fitness applications for both iOS and Android operating systems. The study found that many of the applications transmitted data, without user notification, to third parties. The data transmitted included items like latitude, longitude, and zip code data [3]. Since many of the developers were not medical entities, the data sharing is not limited. The medical data can be used for marketing of products and can be sold to third parties for other uses. This is a large invasion of user privacy and creates one more way to link consumers to their already existing medical data that has been “anonymized.”
Major Stakeholders
The winners here are largely marketing and advertising agencies. After buying a, or using a publically available, dataset marketers can use the few remaining pieces of identifying information like location, age and gender to target specific consumers. With improved consumer targeting, marketing and advertising agencies can increase their revenue and further their own product line. The consumers are also winners depending on how their data is handled. If the data is handled correctly, the profits from the distribution of the data would allow companies, like SafeHeart, to subsidize the cost of the medical devices [4]. Subsidized devices would allow medical technological companies to reach a broader demographic, providing increased public benefit. The data gathered by the consumer medical devices can also be used to enhance medical research providing additional benefit to the consumers [5]. Finally, the collection of data can benefit consumers because it enables improved tracking of diseases among an entire population [6]. If diseases can be detected faster, a large portion of the public would benefit.
Although consumers can reap a large number of benefits from big data in healthcare, they are losers as well. There will be many consumers who do not want their data to be affiliated with marketing or advertising agencies. To these consumers, this is viewed as an extreme invasion of privacy. In addition to the undesired sharing, these users may be subject to the re-identification process. Even though the shared medical data contains few identifying attributes, the remaining information can be used to associate the original consumer with the appropriate medical record [2]. This too in an invasion of the consumer’s privacy, contrary to many of their desires. After consumers, some medical technology entities are also losers. For companies like Safe Heart, the profit from released datasets would reduce costs to the consumer. As a medical company, improving the public’s health is one of their primary missions. The potential that consumers may be re-identified, or targeted by marketing, with the data discourages release. The apprehension to release data limits data available to researchers making them losers as well. If data were released, researchers would be able to expedite research and provide solutions to prevalent health problems [5]. Consumers may resent the release of their data, but those trying to benefit them can produce worthwhile returns.
Summary
Advances in healthcare technology have also given birth to an increase in the amount of big data created by medical devices. Medical big data creates a unique set of legal and ethical issues that companies like Safe Heart must, and are, considering. Legally, acts like HIPAA and the Privacy Act do not sufficiently protect the data of patients. Data can move considerably freely and it is not always transferred in a completely anonymous state. It has been shown that organizations are not handling the data in an ethical manner. The release and negligent handling of the data completely invades the privacy of the patient. For marketers, this aids when trying to increase revenue. Due to many of these issues, companies have started to limit what data they share when medical devices generate it. Without accessible data sets, progress of researchers is slowed and the standard of care for the public falls. Both the benefits and risks must be considered when medical big data is involved.
Conclusions
Health devices transmitting big data are already involved in our lives. It is a serious legal issue that HIPAA and the Privacy Act do not govern our health data properly. It is critical that our laws catch up with this rapidly developing technology. A reasonable person may argue that health data should be completely restricted and there should be no transmission, or distribution, at all. It is true that data laws need to be revisited and improved, but complete restriction would be an extreme waste of the potential that medical big data stores. After the laws have been optimized for the technology, the data has the ability to improve health care throughout the nation. Big data can be extremely useful for entities like hospitals. Using patient data, hospitals can monitor a patient’s condition and know more quickly when they are due to worsen [7]. Advanced algorithms can also predict and help to prevent conditions like renal failure, infections, and negative reactions to drugs [7]. When physicians are combined with big data indicators, more patients can be helped and conditions can be monitored more reliably than in the past. In conclusion, I think that big data in healthcare should be embraced, but not before we strengthen the laws governing it.
References
[1] Kalyvas, James R. and Overly, Michael R. Big Data: A Business and Legal Guide. Auerbach Publications. 55-58.
[2] Anderson, Nate. “Anonymized” data really isn’t—and here’s why not. 9/8/09. http://arstechnica.com/tech-policy/2009/09/your-secrets-live-online-in-databases-of-ruin/
[3] Njie, Craig Michael Lie. Technical Analysis of the Data Practices and Privacy Risks of 43 Popular Mobile Health and Fitness Applications. 7/15/2013 http://www.privacyrights.org/mobile-medical-apps-privacy-technologist-research-report.pdf.
[4] Tanner, Adam. Health Entrepreneur Debates Going To Data’s Dark Side. 9/16/14 http://www.forbes.com/sites/adamtanner/2014/09/16/health-entrepreneur-debates-going-to-datas-dark-side/
[5] Standen, Amy. How Big Data Is Changing Medicine. 9/29/14. http://blogs.kqed.org/science/audio/how-big-data-is-changing-medicine/
[6] Schmarzo, Bill. Big Data Technologies and Advancements in Healthcare. 3/25/14. https://infocus.emc.com/william_schmarzo/big-data-technologies-and-advancements-in-healthcare/
 

Big Data as an e-Health Service

Abstract:
Bigdata in healthcare relates to electronic health records, patients reported outcomes all other data sets.It is not possible to maintain large and complex data with traditional database tools. After many innovation researches done by researchers Big Data is regenerating the health care, business data and finally society as e-Health .The study on bigdata e-health service. In this paper we come to know why the current technologies like STORM, hadoop, MapReduce can’t be applied directly to electronic-health services. It describes the added capabilities required to make the electronic-health services to become more practical. Next this paper provides report on architecture of bigdata e-health services that provides meaning of e-services, management operations and compliance.
Keywords: Introduction to big data, different types of technologies of bigdata, advantages of bigdata, applications of big data, solutions of e-health services, big data as a service provider, e-health data operation management.
Introduction:
What is bigdata?
Bigdata consisting of extremely huge amount of data sets which consists all kinds of data and it is difficult to extract. It can be described by the characteristics like variety, velocity, volume and variability.

– It consists of data like structured, unstructured and semi structured data
– Structured data consists of databases, small scale health personal records, insurances, data wares, Enterprise Systems(like CRM, ERP etc)
– Unstructured data consists of analog data, Audio/video streams. Treatment data, research data
– Semi Structured data consists of XML, E-Mail, EDI.

– Velocity depends on time Sensitivity
– It also depends on streaming

– It may consists of large quantities of files or small files in quantity
– for example , now a days single person can have more than one Gmail account. When he wants to login into a gmail accounts the system generates log files .
If a person login into gmail account multiple times through his different accounts then , the system generates huge number of log files that is stored in a servers using bigdata.

– It shows the inconsistency of data depends on variation of time period .It may be a problem for analyzing the data.
Historically Bigdata in health care industries generate huge amount of electronic health datasets are so complex and difficult to manage by using the traditional software’s or hardware nor by using some database management tools. Now the current trend is to make these huge amount of data as Digitalization so that this whole digital healthcare system will transform the whole healthcare process will become more efficient and highly expensive cost will be reduced. In other words Bigdata in healthcare is evolving into a propitious field for providing perception from large set of data and it produces outcomes which reduces the cost.
Bigdata in healthcare industry is stunning not only because of huge volume of datasets like clinical records of patients health reports, patient insurance report, pharmacy, prescriptions , medical imaging , patient data in electronic patient records etc but also multiplicity of data types and the speed of increasing the records.
Some of the reports generated by researchers on the health care systems shows that, one of the health care system alone has reached in 2011, 150 Exabyte. At this rate of increase of growth, in future the bigdata reaches Zettabyte scale and soon it reaches to Yottabyte from various sources like electronic medical records Systems, social media reports, Personal health reports, mobile health care records, analytical reports on large array of biomedical sensors and smart phones.

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The electronic-health medical reports generated by single patient generates thousands of medical reports which includes medical reports, lab reports, insurances, digital image reports , billing details etc.All these records are needed to be stored in database for validating , integrating these records for meaningful analysis. If these reports are generated by multiple patients across the whole world of healthcare processing system then we have to combine these whole data into a single system which is a big challenge for Big Data.
As the volume and Source of storing the data increases rapidly then we can utilize the e-health data to reduce the cost and improves the treatment. We can achieve it by investigating the big data e-health System that satisfies Big Data applications.
BIG DATA FOUNDATIONS FOR E-HEALTH :
The Following Figure 1 shows the bigdata service environment architecture that provides the support for electronic-health applications from different sources like testing center, individual patients, insurance facilitator and government agencies .All these produces some standard health records are connected commonly to a national healthcare network.

Figure 1. e-Health Big Data Service Environments
Different types of Data sources :
The different types of data sources may include structured database, unstructured datasets and semi structured information

Some of the standard structured data that deals with the drug insurance policy by NCPDP (National Council for Prescription Drug Program) and NCPDP SCRIPT for messaging the electronic prescription for validating the interaction between drug to drug, medical database records, dosage of drug, maintain the records.

The semi structured data related to radiology pictures are changed over the IP networks is provided by DICOM(Digital Imaging and communication in Medicine).
The e-Health system store, gather the medical information, patient information to the doctors unexpectedly includes medical information, vaccination details, diagnostics’ reports.
HDWA Healthcare Data Warehousing Association it provides the environment for from others. They work collaboratively which helps them to deliver accurate results or solutions from their own organizations
A strong relationship and interaction from test facilitators and technical team is maintained within the organization.
We have to face the challenges for utilizing the unstructured data related to different concepts, sharing and accessing the data.

Big data solutions and products:
Bigdata investigation requires knowledge about storing, inspecting, discovering, visualizing the data and providing security by making some changes to some of technologies like Hadoop, MapReduce, STORM and with combinations.
STROM:
STROM is a distributed, open source , real time and fault-tolerant computational system. It can process the large amount of data on different machines and in real time each message will be processed. Strom programs can be developed by using any programming languages but especially it uses java , python and other.
Strom is extremely fast and has the capability to process millions of records per second per node as it is required for e-health services. It combines with the message queuing and database technologies. From the figure 2 we can observe that a Strom topology takes huge amount of data and process the data in a typical manner and repartitioning the streams of data between each stage of process.
A strom topology consists of spout and bolts that can process huge amount of data. In terms of strom components the spout reads the incoming data and it can also read the data from existing files .if the file is modified then spout also enters the modified data also. Bolt is responsible for all processing what happens on the topology , it can do anything from filtering to joins, aggregations, talking to database. Bolts receive the data from spout for processing.

Figure 2. Illustration of STORM Architecture
(ref: https://storm.apache.org/)
Some of the important characteristics of Strom for data processing are:

Fast-It can process one million 100 bytes per second per node
Scalable-with parallel calculations that runs across the machine
Fault-tolerant-if a node dies strom will automatically restart them
Reliable-strom can process each unit of data alleast once or exactly once
Easy to operate-once deployed strom can be operated easily

(ref: http://hortonworks.com/hadoop/storm/)
Hadoop for batch processing:
Hadoop was initially designed for batch processing i.e., it takes inputs as a large set of data at once, process it and write the output. Through this batch processing and HDFS(hadoop distributed file system) it produce high throughput data processing.Hadoop is another framework , runs on MapReduce technology to do distributed computations on different servers.
(ref diagram: http://en.wikipedia.org/wiki/Apache_Hadoop)

Figure 3. Hadoop Processing Systems
From the figure 3 we can observe that a hadoop multi-node cluster , it consists of single master node and slave node. A master node has different trackers like task tracker for scheduling the tasks , job tracker server handles with the job appointments in a order. Master also acts like a data node and name node. The slave node acts like a task tracker and data node which process the data only by slave-node only. HDFS layer deals with large cluster of nodes manage the name node server which prevents the corruption of file by taking the snapshots of the name node memory structure.
Many top companies uses the hadoop technology plays a prominent role in the market.The Vendors who uses Hadoop technology will produce accurate results with high performance, scalability in output and cost is reduced. Some of the companies like Amazon, IBM, Zettaset, Dell and other uses Hadoop technology for easy analysis, provides security, user friendly solutions for complex problems.( http://www.technavio.com/blog/top-14-hadoop-technology-companies)
MAPREDUCE:
In 2004, Google released a framework called Hadoop MapReduce. This framework is used for writing the applications which process huge amount of multi-terabyte data sets in parallel on large number of nodes. MapReduce divides the work loads into multiple tasks that can be executed parallel. Computational process can be done on both file system and database.
(ref: http://en.wikipedia.org/wiki/MapReduce)
MapReduce code is usuallay written in java program and it can also can write in another programming languages. It consists of two fundamental components like Map and Reduce. The input and output generated by MapReduce is in the form of key and value pair. The map node will take the input in the form of large clusters and divides it into smaller clusters were the execution process is easy. Rather Mapreduce provides support for hadoop distributed file system can store the data in different servers. This framework provides support for thousands of computational applications and peg bytes of data.
Some of the important features of mapreduce are scale-out architecture , security and authentication, resource manager, optimized scheduling, flexibility and high availability.
Additional tools are needed to add and should be trained for e-Health files to reduce the complexity because some of the compressed files like electronic-health DICOM picture file should be mapped to a singler Map Reducer so it reduces the BigdData effectiveness. The Hadoop big data applications has imposed a limitations on big data technologies has focused on the applications like offline informatics systems.
4) Programming Tools:
The other solution for the e-Health bigdata is MUMPS, it is an programming tool. MUMPS is abbreviated as Massachusetts General Hospital Utility Multi-Programming System. It is also known as M programming language. M is a multi user and it is designed to control the huge amount of database. M programming can produce high performance in health cares and in financial applications.
M provides simple data considerations in which the data is given in the form of string of characters and the given data is structured in a multidimensional array. M requires support for sparse data.Accorrding to the research done by the scientist in US hospitals they are maintaing the electronic Health records (HER) using M language including Vista(Veterans Health Information Systems and Technology Architecture) which manages all hospitals care facilities run by the Department of Veterans.
(ref: http://opensource.com/health/12/2/join-m-revolution)
In future some of the analytical algorithms are developed to solve the problems faced with the big data applications
Additional e-Health (Big Data) Capabilities:
The additional capabilities provided by the Big data e-Health services are Data Federation and aggregation, Security and Regulatory Concerns and Data Operational Management. The bigdata provides the services which helps to organize and store the huge amount of data. Those data is is digitalization , consists of large amount of datasets consists information related to patients all reports.
1) Data Federation and Aggregation:
Data Federation is a type of software which collections the data from the multiple users and integrates the data.Typically traditional software cannot given the solution to store the huge amount of data in hardwares or by some database management tools.But the Data federation will provide a solution based upon the bigdata architecture is based by collecting the data inside and outside of the enterprise through the layer.
Some of the important data federation tools are Sysbase federation, IBM InfoSphere Federation server and so on.
(ref: http://etl-tools.info/en/data-federation.html)
2) Security and Regularity Concerns:
Security is one of the important requirement to describe bidgata e-health services.Security plays a important role because patient share their personl information with the doctors which help the physician to give the correct treatment
3) Data Operational Management
 

Elon Musk big bets

Elon Musk big bets

Introduction

Elon Musk usually starts something by having in mind that his goal is not to have profit but to reveal the possibilities. He is driven by a desire to discover as well as taking big bets.

Nowadays only USA, Russia, China and Elon Musk have launched to space capsule into orbit and successfully brought it back which is one of his biggest bet. Of his career. He is a man with unlimited ambitions because his mind needs to be consistently filled and the bets such as Tesla, PayPal, which he takes, need to be challenging.

Background

•    Elon Musk’s Ventures

Elon has moved from achievements to all the time, bringing to the world a fresh idea of inventions. He has made solar energy affordable for the public through his company Solar City and has made the dream of an electric car possible and inexpensive with Tesla models. This was not enough for him. His SpaceX space travel adventures have proven to be a major success with a promising future. Despite Elon Musk big bet, today he is considered as the most successful daring entrepreneur who has always tried hard to make the world a better place to live. Elon was born in 1971 in South Africa. Milner (2018) proposes that he taught himself computer programming at an early age and sold his first programme years later. He also created a space game called BLASTAR and later he moved to California to pursue a PHD in energy physics at Stanford University. Next, he launched a software company called zip 2 which was basically an online guide for newspapers publisher as well as having a contract with major players in the industry including The New York Times or Chicago Tribune but things were not very smoothed at zip 2 due to relevant issues. At that time, Elon Musk wanted to be the CEO of the company but the Board sold the company to Compaq for three hundred million dollars along with 30 million dollars in stock options. He later received twenty million dollars for his 7% stake in Zip 2. That same year, he co-founded X.com, an online banking company using ten million dollars from the sale of zip 2. A year after that with the dot-com bubble of tech companies closing their virtual doors, X.com acquires shares with another online financial services firm called PayPal. PayPal became the online payment system of choice with millions of users in just a few months but the board again confront him over CEO position due to technical arguments concerning the future architecture of the service. This time eBay bought PayPal for almost two billion dollars. Later Musk received almost 200 million dollars for his 12% stake in PayPal. He then began to dream up that he could land on planet MARS. He decided to start a company that will build an affordable rocket using vertical integration and a modular approach of software engineering. Simmons (2015) proposes that Those ideas bought to life the launch of SpaceX. This was perhaps his greatest vision and he spent the majority of his net worth between SpaceX and Tesla. Although he faced the worst crisis in his career when all of his three companies started collapsing. Indeed things that appear useless had to be closed down and he had to close Tesla sales. Later he decided to raise 40 million dollars out of his own pocket to keep the companies going and maintain the dream alive. Most of his innovative ideas and visions have become true because he never limits himself to dream big.  Beyond headlines and profits, Elon made Big Bets that were quite successful and increase his popularity in the tech world.

Evaluation of Elon Musk Big Bets

•    Tesla and SolarCity

Tesla is a revolutionary idea from the mind of Elon Musk who built an industrial empire of cars, solar energy and rockets. The reason Elon Musk put so much time and efforts to create Tesla was because he thought that transportation should go electric. Tesla was the first auto industry start-up in decades and the only one born in Silicon Valley. Tesla’s plan was to introduce a high quality and high-performance product to first detract buyers and then make an affordable car for the masses. The big issue with Tesla as for electric cars had been the batteries, their cost and how long they last.

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The issue was that there is an unprized externality in the negative effects of gasoline and on the environment as well as national security because we can’t rely on the market industry. In order to have electric vehicles as the long-term transportation mechanism, he has to reach the gap with innovation and Tesla serves as a catalyst to accelerate the day of electric vehicles. Solar City is on the energy production site because it doesn’t help if we have a sustainable consumption of energy but then that energy should be produced in a sustainable way. Solar power could be the single largest source of electricity generator by the midpoint of the century. The SolarCity is all about sustainable energy creation where Tesla is about sustainable energy consumption.

In fact, it’s just a simple extrapolation of the growth of solar power energy. We could also consider that the Earth is entirely a solar power due to the Sun and the ecosystem powered by it.

•    SpaceX and Neuralink

Seen as a catalyst by many American citizens, Elon Musk has a big vision to enable human civilisations to leave the planet earth and that’s going to require funds but he knew that he has enough funds to do this bet. He has the outrageous idea that private enterprise could actually reenergise space travel and in the year 2002, he founded Space exploration technology or simply SpaceX.  He started this company so that human can colonize Mars and save the Earth. His idea was not how to get out into the orbit but how to get there cheaply and he knows that rocket has not evolved since the seventieth century. He came out with low-cost ways to produce engines, to launch operations differently and inject some inventions in other areas. Basically, SpaceX tends to take offshore technology or stuff that was developed by NASA years ago and produced something from it just by figuring out the valuable pieces.

Solutions & changes

•    Alternative Battery Technologies

By 2007, Tesla was running out of money and Elon Musk has to solve the issue alone. He got to a point that his company had only enough money in the bank for a couple of months and there is nobody around who was willing to put more money in Tesla.

He makes some dramatic changes and essentially reforming the business as well as investing all his reverse capital for Tesla. At that point, the key things were to supply battery packs for cars such as Mercedes. He pushed his team to build a smart car with a Tesla electric motor and this was a great solution for the use of alternative battery technologies.

•    Energy Storage and Electric Vehicles

Battery puts power in our devices but they could not unlock the true potential of renewable energy and they could not provide us answer to storing energy when we really need it. The fact is that it’s not just carbon dioxide that causes environmental issues but we have to look into the potential green solution.  Renewable energy is also different to store so we have to consider carbon caption energy that lasts longer and that is powerful. Although Batteries are set to be the key to the green energy revolution powering higher energy performance, road vehicles and also to unleash the true power of renewable energy in order to achieve that, we need the next generation of batteries. The whole point of renewable energy is to make for a cleaner environment and also save the planet and that’s why Elon Musk wants to built electric vehicles.

Recommendations

My recommendation is mainly driven by my belief that if we have to survive climate changes or energy issues, we need to develop renewable energy. Indeed, it is a big bet but technology pushes the boundary and the expectations like never before. We could actually build lithium battery powered by wind and solar power in order to help stabilize the power light. I think like Steve Jobs, Elon Musk is a perfectionist in the art of engineering because he is changing the car the way Jobs change the phone.

Finalizing Elon Musk Big Bets

To conclude, If Elon Musk had his way, the world would be totally powered by renewables and he would be colonising Mars because he is a big picture man with big plans and maybe his quest for the lithium battery would prove to be one of the most significant tips in electric revolution vehicle.

References

Vance, A. (2016). Elon musk – tesla, spacex and the quest for a fantastic future : Book summary. Place of publication not identified: Primento. (2016). Retrieved December 11, 2018, from INSERT-MISSING-DATABASE-NAME.

MILNER, Y. (2018). Elon musk: The visionary. Time International (atlantic Edition), 191(16/17).

Sizing up Elon Musk’s big bet on the Big Frickin’ Rocket … (n.d.). Retrieved from https://www.geekwire.com/2017/sizing-billionaire-elon-musks-big-bet-spacexs-big-frickin-rocket-future/

Baldwin, D. B. (n.d.). Elon Musk’s Big Bets. Retrieved from Module

https://hbr.org/product/elon-musk-s-big-bets/717431-PDF-ENG

(n.d.). Retrieved from https://www.bloomberg.com/news/videos/2014-12-05/elon-musks-big-bet-on-battery-technology

Elon Musk’s Big Bets Harvard Case Solution & Analysis. (n.d.). Retrieved from https://www.thecasesolutions.com/elon-musks-big-bets-case-solution-80348

Simmons, M. (2015, November 24). What Self-Made Billionaire Elon Musk Does Differently. Retrieved from https://www.inc.com/michael-simmons/what-self-made-billionaire-elon-musk-does-differently.html

Lambert, F., Fred, & Electrek. (2018, May 04). Elon Musk warns people betting against Tesla of a ‘next level short burn of the century’. Retrieved from https://electrek.co/2018/05/04/elon-musk-tesla-shorts-warning-short-burn-of-the-century/

Case Study on Big Pharma’s Marketing Tactics

Case Study On The Big Pharma’s Marketing Tactics In The Pharmaceutical Industry
Facts And Assumptions
The term ‘Big Pharma’ is a terminology used to refer to the pharmaceutical industry. The name relates to people’s strong belief that it has played an active role in the ever increasing complicity and costs of health care. There is a crisis in the health care sector and it is believed that the Pharmaceutical Companies have abandoned science and resorted to salesmanship. No reasonable progress is currently being made in the industry due to the negative perception created by unscrupulous marketing strategies being employed (Archie 2009). Doctors should prescribe drugs to patients but are never expected to do marketing and advertising of their products and services. The other fact in the case study is that unethical business practices such as Pfizer’s off-label marketing practices and undue influence to medicines that should be prescribed by doctors to patient, normally lead to a likely increase in the cost of medical services due to the unfair and unleveled competition. The assumption among most people, which is not the truth, is that all pharmaceutical companies engage in dubious marketing tactics. The other assumption is that the Big Pharma companies sell unethical drugs. However, the truth is that the company sells ethical drugs even though the marketing strategies employed by the company are of questionable standards.

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Major Overriding Issues
There are a number of problems in the case study.  One of the overriding issues is that Pfizer Company illegally did the marketing of Neurontin anti-seizer drug. The fact that pharmaceutical industry has decided to lay a lot of emphasis in the marketing of pills and other related drugs rather than developing new crucial ones is an issue of concern too. Drugs firms have also been accused of marketing drugs for purposes that have not been prescribed by the renowned drug and food administration department. Accusations of off-labeling illegal marketing policies and other related marketing wrongs have also emerged as a major problem in the industry. This is a very serious problem which has compromised the integrity and ethical standards of doctors and the entire pharmaceutical industry.
Sub-issues And Related Issues
The ‘Big Pharma’ companies have been accused of participating in unethical business practices. Medical students have been hired to companies even before they graduate from their medical schools.  This is a likely act of poaching talented skills in the market without following the right recruitment process and laid down procedures and policies in the industry. Furthermore, methods such as offering doctors underserved holiday packages, expensive gifts and other kickbacks, have been used to compromise the integrity of the industry as doctors who are given these favours are expected to recommend the specific company’s drugs thus boosting the performance of the company’s in the competition. The costs of drugs have continued to skyrocket due to lack of control of the market practices.
Analysis And Evaluation
Medical practitioners such as doctors, pharmaceuticals, governments and other law enforcement agencies, pharmaceutical companies, and the entire public that seek medical services are the major stakeholders in the industry. The government’s stake in the case is its responsibility and duty to ensure that their subject receive the right medical services they deserve. People have a right to a healthy life and proper medical services. The government also benefit through the taxes paid by other stakeholders in the pharmaceutical industry.
The doctors’ stake in the industry is based on the fact that for them to grow in their career and attain self fulfillment, they have to ensure that the sick people get proper medical care. Their salaries and duration of being employed would also be determined to a greater extent by the standards they maintain in the market. Expensive medical serviced would lead to a decline in customers due to very costly services hence low income. The pharmaceutical industry, research institutes and shareholders in the company have an interest of maximizing profits and making the highest gains possible in their various investments made in the industry. Unscrupulous and corrupt means being employed by the pharmaceutical companies and some medical practitioners compromises the integrity and ethical standards of the industry. It also leads to decline in the performance of most pharmaceuticals organizations.
CSR Analysis
Pfizer Company has the responsibility of ensuring that it regulates the costs of medical services and products under its line of operation. Good pricing would also make more people to receive better healthcare services since they would be in a position to afford the prescribed drugs. It also has the ethical responsibility of using ethical marketing tactics that would benefit both the company and the community of persons who uses its products. Legally, the company must ensure that it follows the due marketing process, price regulation standards, marketing policies hence fully adhering to the rule of law. Such efforts would lead to fair competition in the marketing thus other pharmaceutical companies (competitors) in the industry would have an opportunity to compete on a fair platform. The government would receive the necessary taxes while doctors would live a more fulfilled life with good salaries and wages being received. Proper CSR would help in facilitating efforts to reach a solution to the health care problem.
Evaluation
The case mainly involves Pfizer Pharmaceutical Company in its use of very questionable marketing tactics. The company used very unscrupulous methods in marketing the Neurontin anti-seizer drugs and doing off label-marketing of the painkiller Bextra and other medicines in order to lure more customers into using their medicines. Rather than employing such unfair and unethical marketing tactics, the company should instead have followed the normal marketing channel of producing medicine and simply alerting the medicine practitioners through various existing hospital departments of the existence of their medicines. It is indeed the discretion of the doctors, upon their tests and knowledge, to choose which medicines to prescribe to the patients without any undue external influence.
Recommendations And Implementations
Various relevant laws and policies need to be put into practice. This would ensure easier market control measures meant to ensure that any unethical practices in the industry are curbed. Guidelines on how marketing by pharmaceutical companies should be carried ought to be clearly stipulated. In order to ensure ethical practices are adhered to, education and public awareness forums should be carried to the stakeholders. Doctors should also be protected against selfish companies that demand, threaten and force them into prescribing certain company drugs. Control mechanisms of the cost of healthcare also need to be put into place in order to ensure that customers are not exploited by selfish and unscrupulous medical practitioners. In addition to that, serious penalties and in some instances, nullification of trade licenses should be enforced to companies and individual persons who use unethical marketing tactics.
Appendix: Stakeholder Map
Work Cited
Archie B.C. Business Ethics: A Brief Readings on Vital Topics, a collection of his columns. Athens Banner-Herald Publishers, 2009
 

Big Skinny Online Marketing Development

Big Skinny Case Analysis
Executive Summary
In 2010, Big Skinny CEO Kiril Alexandrov was looking to transcend from retail distribution and print advertising to the world of online marketing to achieve maximum growth. The retail sales pitch was an easy one, as Alexandrov focused on the value of the wallet and the impulsiveness of consumers (Benjamin & Kominers, 2012). Unfortunately, translating this type of sales pitch was much harder to do in the world of cyberspace.

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Big Skinny centered their online marketing efforts around display Ads, keyword searches, social media and relationships with online distributors and deep DISCOUNTED sites such as Amazon and Groupon respectively. The expansion caused much hardship, as Big Skinny received negative feedback on the review website Yelp that stemmed from their Groupon experiment. They also faced a glitch in their online promotion that allowed 4,000 people to order free wallets from their online store.
Big Skinny needs to refocus their online marketing strategy by getting rid of display Ads, refining keyword searches and severing ties with deep DISCOUNTED sites. Big Skinny can create value for their product and manage their orders better by being more selective with who distributes their product and by keeping the price steady. A more seasonal approach surrounding keyword searches can create new revenue from those who are looking to make quick and impulsive purchases. Lastly, by being responsible for who distributes their products, Big Skinny can deliver their product in prompt and timely manner, which will resolve the majority of customer complaints against Big Skinny.
Problem Statement
Despite successful in-person sales campaigns, Big Skinny struggled to find an effective online marketing platform that would grow and connect them to their consumer base. Big Skinny also ran into glitches with their current online marketing campaigns that brought unwanted negative attention and resentment towards the company.
Data Analysis
When Big Skinny transcended into the world of online marketing, it had to develop a way to attract visitors to the website while attempting to convince these visitors to buy wallets. Since most of their wallets were being sold at trade shows or retail stores that centered on a straight-forward approach regarding impulse and value, the translation of this strategy to the internet proved to be a tall task.
Big Skinny looked at various means of advertising such as display ads, algorithmic search, sponsored search, A/B Testing and social media. Display ads offered a two-frame animation; however, the click-through rate of general display ads in 2009 was only .1% (Bejamin & Kominers, 2012). Algorithmic searches use algorithms that the search engine deems most relevant to the user’s query. The websites that most resemble the query appear the highest on the search engine’s list. Sponsored searches use keywords that the advertisers specify that they want to target. These are mostly sold on a “per-click” basis; however the company loses money if the clicks aren’t converted into sales. A/B testing is a marketing technique that shows different advertisements to different users to compare the response rates between the two. Lastly, social media utilizes websites such as Facebook and Twitter to try and create an interactive relationship with consumers.
Alternatives

Big Skinny could eliminate their means of online distribution and PAID ONLINEmarketing, only utilizing social media and their website to conduct advertising and business transactions.
Big Skinny could be more selective in their selection of online distribution, while tailoring their paid sponsored searches to generate interest and sales.
Big Skinny could scrap their online marketing plans, with the exception of social media, and reallot their advertising money strictly on deep DISCOUNTED sites like Groupon and Living Social.
Big Skinny could focus their efforts on expanding in more brick and mortar retail stores by target marketing towards different demographics. They could use traditional media such as TV and radio to drive these efforts.

Key Decision Criteria

Increase customer satisfaction and corporate image
Increase sales and market share
Improve (or at least maintain) profitability
Ease or speed of implantation
Be consistent with corporate mission or strategy
Within our present resources or capabilities
Within acceptable risk parameters
Minimize environmental impact
Maintain and build employee morale and pride

Alternatives Analysis
1. By limiting their online marketing to free social media sites such as Twitter or Facebook, Big Skinny can greatly reduce their marketing costs. With display advertisements only getting clicked through .1% of the time the money is essentially thrown away. Investing in A/B testing requires the hiring of a permanent person and huge overhead. Getting rid of online distributors allows Big Skinny to eliminate the 7-15% commission they pay to Amazon and eBay while being able to manage their order load. Social Media is more than enough because 71% of social media participants say they are more likely to purchase from a brand they follow online. 91% of local searchers say they use Facebook to find local businesses online (Bennett, 2013). The cons of this are that they are missing out on a lot of potential customers by eliminating Amazon and eBay. While ONLINE PAID marketing can be expensive, there is still benefit to sponsored searches. Some of the cost per conversions are profitable and by completely eliminating these searches would be throwing away potential opportunities.
2. The pros of Big Skinny being more selective with their online distributors allows for a happy customer base. There have been several negative reviews on the Yelp site regarding slow delivery and non-existent customer service. By eliminating deep discounting sites such as Groupon, Big Skinny can manage their order load and keep customers happy. Big Skinny would also keep the revenue from the top paid sponsored searches rather than eliminating them all together. The negatives of this are that Big Skinny could miss out on a lot of revenue by not using Groupon or Living Social. They could also miss out on the repeat customers that are generated by these sites as well as missing out on the people who want to try their product without having to pay full price.
3. Instead of eliminating sites like Groupon and Living Social, Big Skinny could embrace the huge influx of customers that it brings. According to the customer satisfaction and analytics company ForeSee, 91% of customers have already or plan to conduct business with the merchant since buying the deal (Bedigian, 2013). This strategy generates a large influx of customers in a short time while attempting to generate residual income by repeat customers. The cons of this are that company’s often lose money during the initial Groupon. The product is discounted by 50% or more and then Groupon takes a 50% commission on the sale price, which leaves the seller receiving only 25% of the original selling price of the item (which in some cases is less than the cost of the item). Forbes has found that 1/3rd of businesses have lost money on a Groupon deal and there is no guarantee that the customers ever return to pay full price from the merchant again (Gleeson, 2012).
4. The pros of using a more traditional advertising medium such as TV or radio would bring brand recognition for Big Skinny. Big Skinny has always had success selling in retail stores because they market their products based on value and impulse. By putting the product in more retail stores, there is a greater chance people will put it in their hands and buy on impulse. Instead of targeting just one big audience, Big Skinny should advertise by target market such as Big Skinny Sport or Big Skinny Women. By doing this they could partner with big retail chains to get into more stores and generate more revenue the old fashioned way. The average time an American spends watching TV is 5 hours compared to just 1 hour browsing the internet, which leads for greater exposure. The cons of doing this are that TV advertising is much more expensive than online marketing (Nielsen, 1997). Another con is Tivo allows people to record their favorite shows and then fast-forward past the commercials. The last con is that TV advertising seems to be a thing of the past, as the amount spent on TV advertising was only up 4.5% in 2011 as compared to 21.7% via online marketing (Gleeson, 2012).
Recommendations
Based on the data, it is best for Big Skinny to be more selective of their online distribution, while tailoring their paid sponsored searches to generate interest and sales. In regards to online distribution, Big Skinny should keep eBay and Amazon, however, should drop deep-discount sites such as Groupon or Living Social. To offer a Groupon deal, Big Skinny is guaranteed to be taking a loss. To be eligible to offer a Groupon, Big Skinny must discount the price of their wallet by at least 50%. This turns a $20 wallet into a $10 wallet. Groupon takes a commission of 50% on the sale price, which leaves Big Skinny walking away with only $5 for every wallet sold (Bice, 2012). Essentially, they are taking a loss with every wallet they sell on Groupon. The goal of a Groupon is to try and get repeat customers; however, the people that use Groupon are bargain-hunters. They won’t return to Big Skinny, but rather, they will return to Groupon again looking for another bargain deal. By using Groupon, Big Skinny also decreases the value of their brand (Gibbard, 2011). Why would a customer pay full price for a $40 wallet when they just bought it on Groupon for $15 or $20 just a short time ago? In addition to dropping Groupon, Big Skinny needs to manage their online distribution better because of customer satisfaction issues.
On the review site Yelp, Big Skinny’s wallets are only receiving a rating of 2.5 out of 5 stars. A lot of the reviews include gripes about not receiving their order for 3-4 weeks or non-existent customer service (most of the negative reviews are from users who bought a Big Skinny wallet on Groupon). If the online distributor doesn’t ship your product in a timely manner, your company risks a tarnished reputation. Whether Big Skinny didn’t have enough stock to fulfill orders or whether Groupon didn’t ship the products in a timely manner, Big Skinny is taking the fall and abuse from customers. When people do research for a product they are going to see Big Skinny’s products with poor ratings. These poor ratings can scare potential customers away. Big Skinny should only use Amazon, eBay and their website to sell their wallets. This allows them to manage their inventory, not get behind on orders and make sure their product gets shipped in a timely manner. Big Skinny has excellent Amazon ratings and should continue to grow their product through the sterling reputation of Amazon. They should sell the product for a higher price on their website so that people are encouraged to buy through Amazon. This is a win-win for Big Skinny because if people buy through Amazon then Big Skinny doesn’t have to waste time and effort fulfilling and shipping orders. If they choose to buy direct than Big Skinny receives a larger profit on their wallets.
Lastly, Big Skinny needs to tailor their sponsored keyword searches. They need to eliminate the term “leather wallet.” They don’t manufacture a true leather wallet and the cost per conversion for this keyword is a sky-high $20.26. Big Skinny should also bid less for the term “thinnest wallet.” The cost per conversion for “thinnest wallet” also has a high cost, which is $10.53. After replacing leather wallet and lowering the bid for thinnest wallet, Big Skinny should add keywords centered on holidays. Wallets are popular gifts on occasions such as Father’s Day and Christmas. Big Skinny should add season keywords such as “Father’s Day Wallet,” “Wallet for Dad,” “Best Wallet for Gift” and “Wallet for Christmas.” This will bring seasonal shoppers into the mix who are looking to spend quickly and impulsively.
Action and Implementation Plan
CEO Kiril Alexandrov will be responsible for delegating the following tasks. The Director of Marketing will pull any promotions or future plans with deep discounted sites such as Groupon or Living Social. The Director of Marketing in combination with the Director of Product Management will reach out to all of those who left negative reviews on Yelp to satisfy the customer complaints and retract the negative ratings. The Director of Sales will carefully select the online distribution channels which Big Skinny will sell through. Big Skinny will only sell through Amazon, eBay and any online outlets of the retail stores that they are currently featured in. The Director of Sales will also raise the prices of wallets on the Big Skinny Website by 10-15% to create value for the product and promote customers to purchase through the select online distribution. Doing this saves Big Skinny the time it would take to fulfill and pack orders, however, if a customer decides to purchase direct, then Big Skinny recoups the 10-15% it would pay Amazon or eBay to sell and fulfill the order. This new price point will be conveyed in a message from the Director of Sales to Big Skinny’s distribution channel.
References
Bedigian, L. (2013). Does Groupon Help Businesses Thrive or Bury Them Alive?. In NASDAQ. Retrieved June 12, 2013, from http://www.nasdaq.com/article/does-groupon-help-businesses-thrive-or-bury-them-alive-cm243672
Bennett, S. (2013). 6 Amazing Social Media Statistics For Brands and Businesses. In Media Bistro. Retrieved June 12, 2013, from http://www.mediabistro.com/alltwitter/social- media-facts_b40978
Bice, B. (2012). Groupon Isn’t a Good Deal for Businesses. In CNBC. Retrieved June 12, 2013, from http://www.cnbc.com/id/49092709
Donnelly, T. (2011). How Groupon Can Boost Your Company’s Exposure. Inc. Magazine. Retrieved June 14, 2013, from http://www.inc.com/guides/201101/how-groupon- works-for-small-businesses.html
Edelman, Benjamin, and Scott Duke Kominers. “Online Marketing at Big Skinny.” Harvard Business School Case 911-033, February 2012. (Revised from original February 2011 version)
Gibbard, J. (2011). Considering Offering a Groupon? Read This First. In Social Media Today. Retrieved June 12, 2013, from http://socialmediatoday.com/jgibbard/337550/considering-offering-groupon-read-first
Gleeson, B. (2012). TV Advertising VS Digital Marketing. Forbes. Retrieved June 12, 2013, from http://www.forbes.com/sites/brentgleeson/2012/11/20/tv-advertising-vs-digital- marketing
Nielsen, J. (1997). Why Advertising Doesn’t Work on the Web. In Nielsen Norman Group. Retrieved June 13, 2013, from http://www.nngroup.com/articles/why-advertising- doesnt-work-on-the-web
 

The Big Band Swing Era

It is said by historians, that they believe the Big Band Swing era dates back as early as the 1920s in early routes of jazz. It wasnt until the 1930’s ranging into the 1950’s, when the Big Band era became more known. Although it is called Big Band, its name can often be misleading. A Big Band consists of an orchestra with anywhere from six to as many as twenty-five musicians. Each band varies with the amount of musicians. The Big Band Swing era got its name based off of the smooth jazz beat and dance that is incorporated with it. The terms jazz band, jazz ensemble, stage band, jazz orchestra, society band and dance band can be used to describe a specific type of a big band. (Wikipedia: Big Band )

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In 1932, a dance orientated band, Duke Ellington composed and recorded a song called “It Don’t mean a thing if it ain’t got that swing” (The World Book Encyclopedia: J,K). The name of this song says a lot about how popular and important the Swing Era was too many people. This type of music was especially important during the time of World War II and leading America out of the Great Depression, by lifting morale. Today, The Big Band Swing Era is known for its unique components and style, which still holds a special place in hearts of millions of Americans.
There are two famous Big Bands that stand out among several other Big Bands. They are the bands of Glenn Miller and Tommy Dorsey, who also played with his brother Jimmy. Glenn Miller’s band, also known as orchestra, was distinctive in a way that he combined the sounds of the clarinet with four saxophones. Glenn Miller said, “A band ought to have a sound all of its own. It ought to have a personality.” (The World Famous Glenn Miller Orchestra) Glenn miller was an excellent trombonist and believed very strongly in the music he performed. During the time of 1926-1938, he played the trombone in several bands including the band of Tommy and Jimmy Dorsey. In 1935, he recorded for the first time under his own name. Some of his well known hits were Sunrise Serenade, Moonlight Serenade and Wishing (Will Make It So). On April 13, 1940, he played at Sunnybrook Ballroom located here in Pottstown, Pennsylvania. It wasn’t until the year of 1942, when he entered the U.S Army. While in the Army, he disappeared during an air journey leaving his music behind.
The band of Tommy Dorsey was also known as the Dorsey brothers. The Dorsey brothers played in bands together as early as in the 1920’s. Tommy Dorsey played both the trumpet and trombone. Throughout the Swing era, Tommy Dorsey was ranked among the top two or three big bands. His orchestra had about fifteen top ten hits in the late 1930’s. The Dorsey Brothers broke the charts with their recording of Coquette. Also, with their recording with Bing Crosby called “Lets Do It” (lets fall in love), broke into the top ten. By this time, they had one of the hottest bands in the country. In 1935, Jimmy left Tommy to go on and play music on his own. The Dorsey brothers both passed away in the late 50’s. As well as Glenn Miller, the Dorsey Brothers also played at the Sunnybrook Ballroom.
Instruments found in big bands were trumpets, saxophones, trombones, drums, pianos, acoustic bass, and guitars. Instruments varied depending on the bands instrumentation of choice. Composers, arrangers and band leaders would switch things up and use more or fewer players in each section. The sections consisted of brass, string, percussion and vocal. Music of the big band was written in strophic form with the same phrase and chord structure repeated several times. In big band music, we also see a chorus that follows the twelve bar blues form, or thirty-two-bar following a (AABA) song format. Solos were also a part of most big bands.
Swing dancing itself was one of the main components of the swing era. The music was represented in the dance, as dance partners (male) would twirl their partners around. Swing dancing is a lively dance which takes up a lot of energy. During this time period, swing dancing released tensions of the depression and the world in war times. This dance is intended for all ages. Some types of dances incorporated with the swing era are; Fox trot, Jitterbug and the Charleston.
Big Band “Swing” still exists today. In fact it is becoming popular again, much like it was in the 30’s throughout into the 50’s. Swing Kat Entertainment located at the Ballroom on High in Pottstown, offers lessons on dances that strived during the swing era. On occasion, they have live Big Bands that perform; mostly bands that replay such music, like that of the Dorsey Brothers and Glenn Miller along with several other composers and songs. Sunnybrook Ballroom, also located in Pottstown has events that get the community together to re- shape and make today’s generation aware, as well as to be a part of the swing era. An organization called The Philadelphia Swing Dance Society holds swing dancing events at the Commodore Barry Club located in Philadelphia. Sometimes big band can be heard playing on the radio on wxpn’s station 88.5. Many people enjoy this type of music and swing dance that’s involved. The younger population also participates in events and enjoy getting their swing on.
Many people enjoy reliving the swing era and look forward to community gatherings. Much like during the swing era, many people would come from all over the nation to attend dances and live performances. On April 15th at the Sunnybrook Ballroom there was a swing dance event that marked the 66th anniversary of World War II. During this event the eighteen piece Swing Fever Dance Band performed its annual “USO Canteen Show,” featuring music of Duke Ellington, Glenn Miller and the Dorsey Brothers (Staff).
On Saturday April 16, 2011, I attended a live performance of a Big Band called the Slicked up Nines and swing dance at Swing Kat Entertainment in Pottstown. This band consisted of nine musicians all being men. Each band member was playing a different instrument. I thought this was an awesome experience to hear the instrumentation and witness people swing dancing for the first time. I myself was sucked into the rhythm of the music and felt the dance floor come alive.
Instruments that the Slicked up Nines played were the following; drums, saxophone, bass, trumpet, baritone, guitar, finger tambourines and a wood block. The musicians were very involved with the music as well as the crowd. Two musicians, who were playing the saxophone, would swing their instrument from side to side. The two instruments that I feel that bring the big band alive are the saxophone and bass. The saxophone gives the music its jazz routes and the base sets the pace and rhythm for swing dancing. I was familiar with all the instruments I saw, other than the finger tambourines and wood block. These instruments were not included in the research in which I performed.
Along with hearing music from the swing era, I was also fortunate to experience a few songs based off of west coast swing. The only new instrument I saw with this type of swing was the electric guitar. This gave the music a nice touch. Some songs that the Slicked up Nines performed were slower songs that used less percussion and more bass. One song that stuck out to me the most had lyrics that were catchy. This song had the words “chew tobacco” as a chorus. Not all of the songs that were played had vocal sounds in them.
This was a friendly environment which had all kinds of age groups present. Many people dressed up in clothing which was worn during the swing era, as well as girls/ladies wearing dresses and men wearing dress shirts and cackies. The dance floor came alive as the band and swing dancers got fully involved in the music. The dance floor was built, so that it would bounce as people would dance on it. I found this to be a really cool feature and felt like I was back in the time period of the big band swing era.
At times the music would get really fast as the men would swirl their partners around. Here, everyone danced with everyone and it didn’t matter who, or where you were from. At the end of each song, the gentlemen would dip their partner. I thought that this was an interesting feature to swing dancing. Another component that I found to be interesting was that the songs would end real suddenly. This is much different than most of the songs we find today. Types of dances that I saw were the Fox trot, Jitterbug and the Charleston. Out of these three dances, I found that the Fox trot seemed a little more relaxed then swinging around the dance floor. Overall this was a fun clean event for everyone. I especially enjoyed the live band performance.
During intermission, while the band took a break there was another event that took place. This was called the birthday circle. The person running the event called anyone to the center of the dance floor with a birthday in April. After this, the people remaining in the room gathered around in a circle. Those who were in the center would pick a partner and would switch dance partners. Everyone who wanted to dance with those who shared a birthday in April had a chance too. This was really cool and fun to watch.
Here I saw for myself that the big band swing still exists to a small extent, due to many age groups that find interest in it. For anyone looking for a different taste of music, I would recommend it for anyone. For me this was a fun experience and I feel that the more that people come familiar with the swing era, the more popular it will continue to be today. The swing era is something that will remain in people’s hearts for many more years to come.
 

The Big Five personality traits

Organisational Behaviour: The Big Five personality traits

What is personality? “Personality is defined as the characteristic set of behaviours, cognitions, and emotional patterns that evolve from biological and environmental factors”. (Corr & Matthews, 2009). What I will discuss in this essay is the personality traits of the Big Five or the Ocean model as it is often called, look at the strengths, weaknesses, and how this model can fit the right people to an organisation. The Big Five helps us understand the personality traits of individuals and how they will react to real life circumstances. How a certain personality will fit an organisation, what their potential strengths, and weaknesses may be. Can a job description be designed around a personality and can that personality guarantee results. The Big Five is a researched base model and has five basic dimensions that can group people into the common personality traits by answering a series of questions.

These are the Big Five personality traits:

(Wikipedia, n.d.)

My study material (Robbins. & Judge, 2015, p. 157) it describes the traits as;

Extraversion are normally full of liveliness, enjoy interacting with people they stand out from the crowd. An introvert can be reserved it is not to be confused with shyness, they need less social attention than an extraversion.

Agreeableness is described by Robbins & Judge (2015, p. 157) as somebody that has the tendency to behave in a particular way towards others. They are more open to meet somebody half way when their ideas clash. Where, someone who is disagreeable tend to have their interest upfront.

Conscientiousness Robbins & Judge (2015, p. 157) consider to be planned and have trustworthiness and aim to set the bar high. People who are low on conscientiousness may come across as sloppy.

Emotional stability Robbins & Judge (2015, p. 157) explain are people who are competent with dealing with stress and their character are less likely to feel negative emotions about themselves. Those who do not score well for neuroticism tend to lack belief in themselves and come across as insecure.

Openness to experience are willing to try new things are more imaginative. People who score low in this dimension tend to be “conventional and find comfort in the familiar”. (Robbins. & Judge, 2015, p. 157)

To better understand these traits I undertook the Big Five personality test on Open Psychometrics (OpenPsychometrics, n.d.). I wanted to see if I could relate my results into my everyday behaviour and see if it fitted with the model and test my strengths and weaknesses against it. My results are as follows:

From the results, I wasn’t surprised at how I fitted the model and could see clear patterns from my day to day behaviour, and how it fits my personality.

Robbins & Judge (2015, p. 160) suggests that these traits are what most people would like to score high on as they are seen as “socially desirable”. They go on to say that there are three personalities dimensions “which we all have in varying degrees” that one would consider “undesirable”. As these personalities are negative in nature they are known as the Dark Triad. 

The Dark Triad has three personality traits, Machiavellianism, Narcissism, and Psychopathy. Robbins & Judge (2015, p. 160) describes Machiavellianism as a person who “manipulates others to their advantage” believe the “end justify the means” and uses the power of persuading to their advantage.

Robbins & Judge (2015, p. 160) explains Narcissism in people show traits of “self-importance”, they desire to be the focus of people’s attention, and feel they are entitled to things that others are not. The evidence they provide highlight that people in management positions often score high in this trait.

Psychopathy in organisational behaviour is not an indication of insanity, Robbins & Judge (2015, p. 160) define it “as a lack of concern for others”. The suggestion from the text is people who score high for psychopathy, do not want to go with the social model, are happy to be economical with the truth so their goals are met. They show little remorse towards peoples feeling.

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Now that we have an understanding of the Big Five, we need to look at why organisations would use this model and how it can benefit them. Can an organisation target certain personality types that fit the cultures they have instilled in the company? Will an organisation know how adaptable someone can be by knowing their traits and how they will react in certain situations.

One of the things we must look at is John Holland’s personality-job fit theory which is touched upon by Robbins & Judge (2015, p.171,p.172)  in my course material. There are six personality types that will determine how happy somebody will be in a role and the likelihood of them leaving the position, someone who matches their personality to the job is less likely to leave and will be more content.

(Robbins. & Judge, 2015, p. 171) “Holland’s six personality types” realistic, investigative, social, conventional, enterprising, and artistic.

Realistic people are hands-on and like to give things a go. They like to problem solve as they go and are less inclined to sit back and consider all options.

Investigative people like to know all the facts and figures before making decisions. They like to have a clear picture of what is happening.

Social people enjoy working with others, helping, and developing people and like close relationships.

Conventional people are structured they don’t like disorder, they want to stick within the rules and have self-control.

Enterprising people like to lead have good persuading skills and like to talk.

Artistic people like to work with ideas, people, and think outside of the box. They don’t enjoy the rules or structure.

(Robbins. & Judge, 2015, p. 171)

By answering Holland’s Vocational Preference Inventory a person can be grouped into one of the six personalities and a list of jobs that are suited to them, which the theory maintains will create a happier work environment and one will be less likely to leave.

The next thing that must be considered in Robbins & Judge (2015, p.172) work is the Person-Organization Fit which they noted “researches have looked at matching people to organisations as well as jobs”. The fast pace life and development of new technologies has challenged workforces to be interchangeable between teams and be able to adapt to new circumstances. It is key that employees are suited to the organisations principles rather than just a definite job role. “The person-organization fit essentially argues that people are attracted to and selected by organizations that match their values, and leave organizations that are not compatible with their personalities” (Robbins. & Judge, 2015, p. 172). Robbins & Judge (2015, p.172) suggest using the Big Five will help employees fit a company’s ideas and beliefs helping increase overall job satisfaction and likely to have a lower percentage of turnover. Employees who have the same principles as the organisation tend to be happier and have a loyalty to the organization.

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While investigating these concepts, I came across a very interesting article on the web (Cooper, 2014) where her work told us that if organisations using and understanding the Big Five personality can have a better fit of a person to a specific job role. Where she states that “understanding the personality traits that suit the role you are hiring for” is important, getting the right people to work together will benefit the organisation because they will share the same traits and will have the same values as the organisation cultures.

Relationships between each personality type Robbins & Judge (2015, p.172) expresses it’s as important to match personality types together, and using the “Big Five terminology”  (Robbins. & Judge, 2015, p. 172) to match the right people together from Holland’s Occupational personality types.

 (Robbins. & Judge, 2015, p. 172)

The diagram they include matches each personality the closer one personality is to each other they should be compatibly to work with each other, the personality opposite should clash and would not create a good work environment.

When, are manager is hiring for their organisation they will need to consider the personality type they want to hire, and need to understand what personality types work well together and equally what types will clash and have a negative impact in the work area.

(Wittleberry, 2016) Explains the personality types from The Holland Codes that work well together in her article. An example from her work describes how realistic who is hands on and likes to see their work is compatible with investigative and conventional person. Someone who is investigative likes to “working with others who are grounded” (Wittleberry, 2016). People who are conventional will fit with these two personality groups because they are organized and takes direction well.

A manager needs to know his workforce and where best to employ these people, (Wittleberry, 2016) recommends that a manager plan his/her environment know which personality will fit with a department, but to have a blooming organisation she concludes the manage should put the right people in the right area so they will grow reach their full potential and work day to day with their strengths. This in theory should create a positive climate in the organisation.

As I have discussed there are many strengths to the Big Five and how they can benefit an organisation with having the right fit op employees, but we also need to consider the weaknesses of the Big Five and do they give an employer a clear picture of the person they are hiring.

(Vitelli, 2015) “The general consensus is that personality is shaped by early life experiences and tend to stay stable over time”. Vitelli (2015) describes how our personality is influence as we grow up. He says that life experience can give us a different outlook on life and in some cases change how we think. As, we mature people can tend to have a different outlook on life and hold different values to what one once thought was important.

(TheHRDepartment, 2015) They warn that not to only use the personality testing as a hiring tool the Big Five processes only the personality of a person it does not consider life experiences, that persons goals in life and that you often need to meet the person to get a feel for them and how they carry themselves. They go on to list some of the advantages and disadvantages of doing a personality test.  When an organisation is looking for a new hire having a personality test done can help narrow the field to the types of people they want to hire, it can have a positive impact on the interview as the manager can have questions suited to that person.

Some of the disadvantages  (TheHRDepartment, 2015) of doing the personality test is that in can be a long process and may turn off potential employees. The experiences that they have from other companies and upskilling they receive may contradict the test results. The test is not fool proof questions can be answered that seem more pleasing to the organisation. If an organisation always hire the same types of personality there can be a “lack of diversity”. Tests cost money so an organisation needs to have this budgeted if they decide to go this route. The final point they go on to make is that a future employee on paper make fit the organisational culture and share the same personality they may not bring those traits though to the job. The essential thing they conclude is that the test is not the only factor in the recruitment process, it can help identify the people to bring through to the interview stage and potential future employees.

From the personality test I took as part of this assignment (OpenPsychometrics, n.d.) I saw where my personality falls in The Big Five, I agree with the results, I know that some of them were skills I developed over time though life experiences and roles within some organisations I worked in. Personality can set the benchmark for an organisation and the Big Five can be the foundation to build employees upon. It will not always explain why people act a certain way, or why they do something.

For a hiring manager it’s important they understand the personality dimensions know the strengths and weaknesses of each trait, and how to pair that person to a department in which they will thrive. While personality testing is important it does not mean you will always get the right person for a job, a test will give you an idea of the person you are hiring but, it may eliminate someone that has the right ethics for your organisation.

Works Cited

(Corr & Matthews, 2009) 

(Robbins. & Judge, 2015) 

(Wikipedia, n.d.) 

Big Data in the Aerospace and Defense Industries

Table of Contents

Executive Summary

Introduction

Industry History and Overview

Status Quo of the Aerospace Industry

Analysis

Challenges

Opportunities

Gap Analysis

Direction for Advancement

Discussion

Benefits

Sustainability

Conclusion

References

If history is to serve as an example, today’s newest aircraft platforms will extend 50 years into the future. This is good news for the sustainability of big data in the aerospace and defense industry since big data has already found a home on today’s newest aircraft. As an industry that is heavily dependent on huge amounts of information from a wide array of sensors, the aerospace and defense industry were early adopters of big data analytics and decision support applications. This report examined the role that big data plays in the aerospace and defense industry today, as well as the challenges, gaps and opportunities that are present. The benefits, sustainability and directions for advancement in the industry were discussed as well.

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Big data was found to be most widely used in the industry for predictive maintenance on aircraft. The industry faces challenges with meaningfully using the data collected, as well as with the infrastructure to stream the data. Nevertheless, there are opportunities to utilize big data to expand predictive maintenance, improve data analytics, as well as optimize flight plans and improve air traffic control. One of the gaps in the industry is the lack of streaming critical data, such as data from the flight data recorder, back to ground control using existing technology. Recommendations for the industry included investing in the infrastructure for big data both on and off the aircraft, as well as investing in research to develop better models, analytics and decision making applications. The benefits of big data will allow for more automated aircraft while increasing safety, reliability as well as profit margins. The next generation of aircraft will be designed utilizing big data, therefore today’s investors in the technology will be the leaders of the future aerospace and defense industry.

In 2014, the disappearance of Malaysia Airlines Flight 370 (MH370) with all 239 people on board captured the world’s attention as investigators scrambled to figure out what had happened to the aircraft (MacLeod, Winter, & Gray, 2014). As theories swirled around the cause of the disappearance, pundits asked why aircraft that record terabytes of data do not transmit the data back to the ground, making it immediately evident what the cause of an accident was. With 100 thousand flights a day worldwide, the infrastructure to support this kind of data transfer simply does not exist, but, perhaps the better question is: should this infrastructure exist? (IATA, 2018). The answer to this question requires an analysis of big data and its applications in the aerospace and defense industry (ADI).

The ADI has long been on the leading edge of technology. When it comes to big data aerospace is once again a pioneer in the field. According to one definition, big data differentiates itself from traditional data by having the 5 Vs, which are “huge Volume, high Velocity, high Variety, low Veracity, and high Value” (Jin, Wah, Cheng, & Wang, 2015, p. 59). Consequently, with big data comes big challenges. This report examines the current state of big data in the ADI and provides an analysis of the gaps, challenges and opportunities of big data in aerospace. The benefits provided by big data, as well as their sustainability, are discussed as well. To provide the context of how the industry got to where it is today, first, a brief history of the industry is presented.

Industry History and Overview

The study of flight goes back millennia, however, it was not until the first successful flight by the Wright brothers in 1903 that the aeronautical industry was born. By 1911, aircraft were being used to deliver mail and by 1915 multi-engine aircraft were being used for commercial passenger transport. Technology in the field rapidly progressed and by World War II aircraft were the defining factor of a country’s military might. Coming out of the war, the jet engine allowed for an explosion in commercial air travel, driving the development of larger and more sophisticated aircraft. In parallel, the space race was taking form creating the field of astronautics, which combined with aeronautics to form the aerospace industry. The thousands of sensors and complex systems needed to control aircraft and spacecraft presented some of the earliest challenges of how to handle mass amounts of data.

By the 1990s, the fall of the Soviet Union and the corresponding drop in military spending, along with an economic crisis on the commercial side of the industry, led to a major consolidation among aerospace companies. The consolidations and dropping margins indicated that the industry had entered the mature phase of its lifecycle. Today, most aerospace companies are involved in both the commercial and defense sides of the industry. The modern ADI designs and manufactures airplanes, spaceships, helicopters, missiles, satellites and defense products. Suppliers provide components ranging from sensors, computers and integrated systems, to engines, structures and interiors. Also, maintenance, repair and overhaul play a big role in the industry (Longo, 2017).

Revenue growth for the industry is expected to occur at a rate of 3% through 2023 (Longo, 2017). The ADI is a highly cyclical industry and is currently in a growth phase. The industry generally follows the global economy and has key drivers such as air traffic forecasts, plane orders and plane deliveries (Corridore & Chuah, 2018). While there are hundreds of players in the industry, revenue is strongly consolidated among a few large players, namely, Boeing, Airbus, Lockheed Martin, United Technologies, BAE Systems and Raytheon (Corridore & Chuah, 2018). On the other end, the primary customers of the industry are governments and airlines.

The factors in the general environment that influence the ADI include economic, global, political, demographic and technological components (Corridore & Chuah, 2018). Due to the nature of the ADI, it is unsurprising that it is characterized by high levels of technology change, globalization and regulations (Longo, 2017). Additionally, consolidation has created high barriers to entry and a medium level of capital intensity (Longo, 2017). From Porter’s Five Forces perspective, rivalry is strong due to shrinking margins and fierce competition. The power of the buyer is strong since customers have options, while the power of the seller is moderate as consolidation continues. Finally, the threat of new entrants and the threat of substitute are low due to the high barriers to entry and the lack of realistic alternatives to flying, respectively.

Powerful buyers, high technology demands and expensive regulation place a lot of pressure on ADI companies to find competitive advantages to maintain their margins. The potential applications of big data in the ADI are enormous, and successful implementation can provide a company the edge it needs. The industry has not ignored this potential nor remained idle to the opportunities of big data. In the next section, the current state of big data in the aerospace industry is discussed.

Status Quo of the Aerospace Industry

The ADI is well entrenched in the utilization of big data. The current amount of data produced by the industry is mind-boggling. Take, for example, the typical Boeing 737, the most widely flown commercial aircraft. The engines alone on a 737 record over 240 terabytes of data over a 6-hour flight (Badea, Zamfiroiu, & Boncea, 2018). Figure 1 shows the vast scope of this amount of data when it is extrapolated to all US commercial air traffic. Put into context, the data amount shown in Figure 1 is equivalent to all global data traffic in 2015 (Badea et al., 2018).

Figure 1. Data generated from aircraft engines alone in the US each year

Source: (Badea et al., 2018)

The applications of big data analysis in the industry include areas such as optimizing flight plans, modeling weather effects on flight, determining customer patterns and providing predictive maintenance (Badea et al., 2018). Big data is used in the field to optimize performance and in the lab as feedback in design optimization (Sethi, 2015). Examples include monitoring engine pressure and temperature to increase fuel efficiency, as well as monitoring stress levels and temperature exposure of parts to predict when they will fail (Sethi, 2015). Furthermore, through the use of Internet of Things (IoT) manufacturers are able to track the performance of their operations and improve efficiencies (How Big Data Is Transforming The Aerospace Industry, n.d.).

Of all the applications of big data noted, predictive maintenance currently has the most widespread use. Predictive maintenance utilizes big data to create models that determine the conditions that precede the failure of an aircraft part (Ezhilarasu, Skaf, & Jennions, 2019). The models then recommend to the operator to replace the part prior to a failure occurring, thereby increasing safety and reducing unplanned downtime costs. These systems, called Integrated Vehicle Health Monitoring (IVHM), are currently used on avionics, aircraft engines, unmanned aerial vehicles (UAV), fuel systems, satellites and spacecraft (Ezhilarasu et al., 2019). SAP’s Predictive Maintenance program is an example of one application commonly used by airlines for IVHM (Badea et al., 2018).

While the aerospace industry is ahead of most industries in the use of big data, there are a number of challenges and opportunities the industry has yet to address. In some areas, there are some serious gaps that have drawn widespread criticism of the industry. The analysis section examines these challenges, opportunities and gaps in detail.

The determination to build the Airbus A380, the largest passenger jet in the world, emanated from a joint market study completed by Airbus and Boeing (EASA, 2017; Reuters, 1995). Airbus determined that the market was looking for a high-capacity jet and developed the A380 while Boeing determined the market was looking for medium jets and developed the 787 Dreamliner. In 2019, Airbus announced it would shut down the production of its A380 jets after producing just 30 aircraft at an estimated loss of $25 billion (Airbus, 2019; West, 2014). Meanwhile, Boeing has produced 840 of the 787 aircraft and continues to ramp up production (Ostrower, 2014). Despite looking at identical data sets, the companies drew different conclusions from the data. Unfortunately for Airbus, their misinterpretation cost them $25 billion and 21 years of development.

 Raw data on its own is meaningless. The data must be processed, analyzed and correctly interpreted. Yet correctly interpreting the data is just one component of the challenges that big data presents to the aerospace industry. The analysis that follows digs into the details of the challenges, opportunities and gaps of big data in aerospace, and provides resulting recommendations for advancement.

Challenges

As with industries everywhere, big data is still a developing technology in aerospace (Urbinati, Bogers, Chiesa, & Frattini, 2019). Huge amounts of data are collected in the ADI raising common challenges with data complexity, computational complexity and system complexity (Jin et al., 2015). The largest challenges of big data in the aerospace industry are limitation on models to interpret data, data streaming limitation and realtime use of data.

There is no doubt that the ADI generates astronomical amounts of data, yet the majority of the data goes unused (Badea et al., 2018). Creating models and tools to make accurate use of the data has proved to be a difficult engineering challenge (Taylor, & Waldron, 2019). There is still plenty of research to be done to create models to determine how stress, fatigue, wear and temperature affect the useful life of a part. Until then, predictive maintenance is limited to well-understood areas and components on an aircraft (Ezhilarasu et al., 2019).

When a plane flies over an ocean, satellite streaming is its primary way of transferring data. Satellite streaming is both expensive and limited in bandwidth (Taylor, & Waldron, 2019). This presents a significant challenge for realtime big data use, limiting applications of the data to the capabilities on the aircraft. One resulting limitation is that it prevents the optimization of flight paths based on weather conditions and flight data (Badea et al., 2018). Furthermore, even when the cost investment is made into streaming flight data, concerns over the cybersecurity of the data are a major consideration due to the sensitive security nature of flight (Taylor, & Waldron, 2019). Yet, with challenges come opportunities, which are discussed next.

Opportunities

The wide range of opportunities for the advancement and use of big data in aerospace paints a promising picture for the industry. Predictive maintenance models and algorithms are still in the infancy stage of application; their potential for widespread use on the aircraft is enormous. To date, a large focus for predictive maintenance has been on the aircraft engines since they are the single largest cost items on the aircraft (Ezhilarasu et al., 2019). According to Ezhilarasu et al., the expansion of predictive maintenance from a component level to the overall aircraft level is necessary to accomplish the goal of preventing unexpected downtime (Ezhilarasu et al., 2019). To get IVHM to the aircraft level requires an installation of sensors throughout the aircraft, upgraded analytical capabilities on the aircraft and improvements of in-air data streaming.

An additional benefit of increasing the sensing capabilities throughout the aircraft is that, over time, the data collected will allow for better characterization of failure modes thereby increasing the effectiveness of the IVHM models. This brings up another opportunity – the majority of the world’s aircraft fleet is still made up of aging aircraft with limited built-in capability to handle big data (Taylor, & Waldron, 2019). Proactively upgrading aircraft that may have another 15 years of life ahead of them will help with downtime reductions while improving data gathering for IVHM capability improvements.

Another opportunity is to utilize the data output from the aircraft, combined with ground control data, to reduce air traffic congestion and thereby increase airport turnaround times for aircraft (Badea et al., 2018). Better turnaround times result in higher profitability (Schlesinger, 2011). A similar combination of live flight data with weather data can be used to optimize flight plans inflight, thereby increasing aircraft efficiency (Badea et al., 2018). Both of these opportunities, once again, utilize existing data systems but require new programs to implement the optimizations.

 With the amassed amount of data already collected by the ADI, there is a huge opportunity to put that data to use. The first opportunity this presents is determining what parameters of the data collected is useful. If certain parameters are deemed unneeded, manufacturers can optimize the investment in big data capabilities on the aircraft (Sethi, 2015). The second opportunity presented by this treasure trove of data is using analytics to design the next generation of efficient airplanes (Sethi, 2015). The first-to-market that utilizes this opportunity will likely obtain a new competitive advantage.

Gap Analysis

Today’s digitally connected aircraft record data on over 300,000 parameters (Badea et al., 2018). However, data recovery from a lost aircraft is done through the “black boxes” – the flight data recorder and cockpit voice recorder – technologies that were developed in the 1950s (Yu, 2015). Herein lies a gap that plagues the aerospace industry: while some technologies used are on the cutting edge, there is still widespread use of antiquated technology. In a safety above all else industry, some level of relying on tried and true over new can be expected, but in areas like the black boxes, the industry is just laggard.

The technology to stream black box information is already in place, as evidenced by live data transfers from aircraft engines. Neither continuous streaming nor every parameter is required; a limited number of parameters transferred at regular intervals into big data infrastructure would greatly improve accident investigations and help in locating lost aircraft (Yu, 2019). A simple upgrade would increase public confidence in flying and provide closure to families of accident victims, like those of MH370.

Another gap exists in the maintenance culture of airlines. Even where predictive maintenance powered by big data exists, maintenance personnel are reluctant to follow the recommendations of the software and replace a part that has not failed yet (Taylor, & Waldron, 2019). Obviously, this defeats the purpose of the investment in big data capabilities and demonstrates the need for more training and awareness on big data. These gaps, along with the challenges and opportunities, provide the directions for advancement in the industry, which are discussed next.

Direction for Advancement

Aircraft manufacturers have been combating shrinking margins for some time now (Longo, 2017). They have done this by pressuring suppliers for price concession and suppliers have responded with mergers to provide them with more negotiating power (Corridore & Chuah, 2018). Likewise, the airlines have historically struggled with low profit margins. The investments in big data, therefore, provides strategic alignment with the goals of increasing profit margins. It does this by providing airline customers with ways to reduce their costs through predictive maintenance while providing the manufacturer with an add-on product to sell. Furthermore, improvements from big data align with the demands of the general environment to increase the efficiency and environmental friendliness of aircraft. With ever increasing safety regulations, big data also provides the opportunity for aerospace companies to stay ahead in terms of safety.

As pioneers in the field of big data, the ADI is well positioned to make use of its resources to push advancements in big data collection, analytics and decision making software. For one, the ADI has an unmatched resource when it comes to scientists, engineers and software programmers (Aerospace Industries Association, 2016). This human capital resource puts the industry in a strong position to tackle big data challenges.

The industry has some work to do, however, when it comes to alignment of companies within the industry. Big data in the industry is most useful when it is shared between the airlines and manufacturers (Taylor, & Waldron, 2019). It allows the manufacturers to predict part demands and allows them to improve their designs. However, there is a debate between the airlines and manufacturers over who owns the data, as well as concerns by airlines of sharing data with manufacturers that also serve their competitors (Taylor, & Waldron, 2019). This has resulted in a hesitancy to share data that has the potential to impede progress with big data. Solutions to create alignment in the industry can be as simple as data sharing agreements or even joint ventures between the airlines and manufacturers. This will allow for more rapid improvement to the analytics and decision making software for in the industry.

Big data and its application do face a number of criticisms in the ADI. Some airlines have criticized the focus on aircraft analytics over ground operation analytics, which can provide cost savings as well (Taylor, & Waldron, 2019). Still others level the fair criticism over the mass amounts of data collected, much of which does not provide actionable insights (Taylor, & Waldron, 2019). Of course, there are alternatives to big data.

One alternative, albeit not a very strong one, is to continue using traditional analytics while becoming more aggressive on maintenance intervals. While cheaper in terms of investment, this alternative adopts a short outlook. It will require stocking parts based on historical needs, so will, therefore, defeat the purpose of reducing inventory costs and unplanned downtime. Another alternative is to engineer more robust parts and error-proof the manufacturing process. However, weight and cost increases in this approach can be prohibitive, making this a poor alternative as well. For each use of big data, an alternative can be found. However, the efficiencies and widely encompassing scope of big data provide stronger promise than any of the alternatives proposed to date.

The results that big data have shown so far in the ADI, illustrated that it is a train that cannot be stopped. It is the next step in the technological development of aircraft. What is needed now is one of the large manufacturers to go “all in” and take the helm in advancing the analytics and decision making software so that it can be used at multiple tiers in the ADI (Taylor, & Waldron, 2019). The goal of zero unplanned aircraft out of service is achievable when production, ground maintenance, predictive maintenance, flight plans and air traffic control have been optimized through the use of big data.

Some directions for advancements should be “go dos” for the industry, such as live streaming some aspects of the flight data recorders (Yu, 2015). Dedicating resources to research and development of models to interpret the data coming off of aircraft should be a high priority for the industry. Improvement of the infrastructure, on and off aircraft, to improve data streaming and decision making capabilities, go hand in hand with these models. The advancements gained through big data will allow for further flight automation and reduced costs. As the field advances, the day may come when the idea of a human at the controls in the cockpit will seem arcane. The benefits do not stop at automation alone; the benefits are far reaching and are discussed in detail next.

Lower costs, better designs and more efficient aircraft are among the tangible benefits derived from big data. Customer satisfaction, customer loyalty and an increased perception of safety are some of the intangible benefits derived from big data. The following sections go into further detail on the benefits and sustainability of investments into big data analytics and decision support applications.

Benefits

Maintenance issues cost airlines a lot of money, to the tune of ten thousand dollars per hour of downtime per aircraft (Badea et al., 2018). Clearly, there is a lot of money at stake in keeping aircraft flying. That is why the return on investment for big data analytics and decision support applications has such a strong case for the long term. Predictive maintenance programs powered by big data have already shown promising returns by reducing unplanned downtimes and reducing maintenance costs. The expansion of predictive maintenance will only increase these returns. Additionally, predicting when parts will be replaced also allows a reduction in spare parts inventory.

The benefits of big data in aerospace materialize through cost savings, improved safety and higher reliability. Due to its intangible nature, a price cannot be placed on the effect the loss of consumer confidence has on an airline that suffers an accident. Utilizing big data to improve aircraft reliability, aircraft turnarounds times and reduce flying times will increase customer satisfaction and loyalty, resulting in direct improvements to a company’s bottom line.

The next generation of aircraft will be designed using the data generated from today’s aircraft. The company that best puts to use its big data will achieve a competitive advantage in the industry. Furthermore, retrofitting older fleets will allow them to share in the same benefits as the newer aircraft while also generating economies of scale (Taylor, & Waldron, 2019). The many benefits of big data analytics and decision support applications provide sustainable returns over the long run, as detailed in the next section.

Sustainability

The life expectancy of an aircraft is typically 20 years. With production runs going for over 40 years, the life of a program can be expected to last 60 years. This means that investments into big data that have already been made for today’s newest aircraft have at least another 50 years ahead of them. With a time horizon that large, from a business perspective, this means that big data is here to stay in aerospace.

The future looks even more promising when the current level of investment by the ADI in big data is considered. The competition is fierce to provide big data technology, and the corresponding analytical and decision making services, on the newest aircraft platforms (Peaford, 2018). The aircraft OEMs, suppliers, airlines and even outside services are competing against each other in this arena (Peaford, 2018). Clearly, the industry leaders have recognized the value of big data and are investing huge amounts into their technology and service capabilities. As the technology matures, so will the cost savings. Furthermore, as has often been the case in the ADI, when a new technology demonstrated a higher level of safety, it eventually becomes mandated. All these factors combined bode well for the long-term sustainability of big data in the ADI.

Big data has found a home in the aerospace and defense industry where it has found widespread use in the form of predictive maintenance. Yet, the analytics and support decision support applications to utilize big data lag behind the data collection efforts in the industry, leaving large amounts of data unused. Furthermore, aircraft face challenges with the current capabilities of data streaming infrastructure, limiting the use of big data in real-time. However, opportunities exist to expand the predictive maintenance network on the aircraft, improve the predictive models, as well as optimize flight plans and improve air traffic control.

Gaps exist in the industry as was demonstrated after the loss of flight MH370. Streaming of flight recorder data can be done with existing technology and is an improvement the ADI should implement. Big data technology in its current form would not have prevented the tragic loss of flight MH370, but it could have shed light on why the aircraft crashed, as well as provide the location of the crash, thereby providing closure for the families who lost loved ones. Big data will design tomorrow’s aircraft. The ADI should invest in big data infrastructure on and off the aircraft, as well as invest in research to create better models and decision making capabilities. The results will allow for more automated aircraft while increasing safety, reliability as well as profit margins. Big data has a sustainable and promising future in the ADI. Therefore, the early adopters of big data today will be the industry leaders of tomorrow.

References

Aerospace Industries Association. (2016). The Defining Workforce Challenge in U.S. Aerospace & Defense. Retrieved from http://www.aia-aerospace.org/wp-content/uploads/2016/09/STEM_Report_lowres_V11.pdf.

Airbus. (2019). Airbus and Emirates Reach Agreement on A380 Fleet, Sign New Widebody Orders. Retrieved from https://www.airbus.com/newsroom/press-releases/en/2019/02/airbus-and-emirates-reach-agreement-on-a380-fleet–sign-new-widebody-orders.html

Badea, V., Zamfiroiu, A., & Boncea, R. (2018). Big Data in the Aerospace Industry. Informatica Economică, 22(1), 17-24. Retrieved from https://doi-org.ezproxy.lib.uwf.edu/10.12948/issn14531305/22.1.2018.02

Corridore, J., & Chuah, S. Y. (2018, April). Media. Standard & Poor’s Industry Surveys. Retrieved January 25, 2019 from Standard and Poor’s NetAdvantage.

EASA. (2017). Type-certificate Data sheet: Airbus A380 (Report No. EASA.A.110). Retrieved from https://web.archive.org/web/20180219151308/https://www.easa.europa.eu/sites/default/files/dfu/TCDS_EASA%20A%20110_A380_Iss_12.pdf

Ezhilarasu, C., Skaf, Z., Jennions, I. (2019). The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities. Progress in Aerospace Sciences, 105(1), 60-73. Retrieved from https://www-sciencedirect-com.ezproxy.lib.uwf.edu/science/article/pii/S0376042118301337#sec4

How Big Data Is Transforming The Aerospace Industry. (n.d.). Retrieved from  https://www.opentracker.net/article/how-big-data-is-transforming-the-aerospace-industry

IATA. (2018). Traveler Numbers Reach New Heights. Retrieved from https://www.iata.org/pressroom/pr/Pages/2018-09-06-01.aspx

Jin, X., Wah, B., Cheng, X., & Wang, Y. (2015). Significance and Challenges of Big Data Research. Big Data Research, 2(2), 59-64. Retrieved from https://www-sciencedirect-com.ezproxy.lib.uwf.edu/science/article/pii/S2214579615000076

Longo, D. (2017, August). Aircraft, Engine & Parts Manufacturing in the US: 33641a. IBISWorld Industry report. Retrieved February 1, 2019 from IBISWorld.

MacLeod, C., Winter, M., & Gray, A. (2014, March 7). Beijing-bound flight from Malaysia missing. Retrieved from https://www.usatoday.com/story/news/world/2014/03/07/malaysia-airlines-beijing-flight-missing/6187779/

Ostrower, J. (2014, Jan 7). Boeing’s Key Mission: Cut Dreamliner Cost. https://www.wsj.com/articles/boeing8217s-key-mission-cut-dreamliner-cost-1389143509?tesla=y

Peaford, A. (2018, March 14). Big data: Two little words, huge impact in aerospace. Retrieved from https://www.wearefinn.com/topics/posts/big-data-two-little-words-huge-impact-in-aerospace/

Reuters. (1995). Boeing, Partners Expected to Scrap Super-Jet Study. Retrieved from https://www.latimes.com/archives/la-xpm-1995-07-10-fi-22333-story.html

Schlesinger, J. (2011, July 15). 10 Minutes That Changed Southwest Airlines’ Future. Retrieved from https://www.cnbc.com/id/43768488.

Sethi, C. (2015, Dec 11). Aerospace Bets on Big Data. Retrieved from https://www.asme.org/topics-resources/content/aerospace-bets-on-big-data.

Taylor, E., & Waldron, G. (2019, March 26). ANALYSIS: Five key themes for Big Data in aerospace. Retrieved from https://www.flightglobal.com/news/articles/analysis-five-key-themes-for-big-data-in-aerospace-456869/

Urbinati, A., Bogers, M., Chiesa, V., & Frattini, F. (2019). Creating and capturing value from Big Data: A multiple-case study analysis of provider companies. Technovation, 84(1), 21-36. Retrieved from https://www-sciencedirect-com.ezproxy.lib.uwf.edu/science/article/pii/S0166497218300361#s0080

West, K. (2014, Dec 28). Airbus’s Flagship Plane May Be Too Big To Be Profitable. Retrieved from https://www.businessinsider.com/airbuss-flagship-plane-may-be-too-big-to-be-profitable-2014-12

Yu, Y. (2015). The Aftermath of the Missing Flight MH370: What Can Engineers Do? IEEE, 103(11), 1948-1951. Retrieved from https://ieeexplore.ieee.org/abstract/document/7302632