Emerging Trends in Digital World

Discuss About The Transactions On Knowledge Data Engineering.

The big data, Cloud and Internet of Things are the emerging trends in this digital world. These technologies are not only the required technology but also the necessity in every sector and organization. These technologies although emerged individually however, they have been intertwined (Aly, Elmogy & Barakat, 2015). The increasing digital transformation has resulted in interdependent of these three technologies. The intertwining of these technologies is described as huge demand for big data has resulted in adoption of both Internet of Things and Cloud platforms (Sun et al., 2016). The organization uses big data and are rapidly increasing. The big data along with Internet of Things and Cloud has found many applications in various industries and they are business, healthcare, banking, financial companies and other companies (Fazio et al., 2015). The big data is increasingly day-by-day, it is characterized by three factors, and they are numerous data, data is complex and data are rapidly generated, captured and quickly processed. Cloud Computing is the technology, which is absorbing the bid data aspects to provide a better integration for data and information. The Internet of Things is a valuable technology which visualizes, uncovers insights from various types of data such as structured, semi-structured and unstructured (Marjani et al., 2017). The Big Data produces huge number of data and they are complex to identify and evaluate. The challenges related to Big Data in Internet of Things are making sense of the complex data, identifying consumed data and taking actions on the data (Da Xu, He & Li, 2014). The challenges of big data briefly describing in Internet of Things are as follows. The first challenge is representation of data that contains datasets and have certain levels, semantics, structure, granularity, industry and accessibility (Yi, Li & Li, 2015). The representation of data should be proper, as improper representation of data will minimize the effective data analysis. Hence, representation of data is necessary to provide a well-analyzed data for future operations. The second challenge is reduction in data redundancy and compression of data (Aly, Elmogy & Barakat, 2015). Generally, the datasets has high level of redundancy that needs to be compressed. The data after reducing redundancy is filtered and compressed to get the actual data that is relevant to the organization. The third challenge is management of data life cycle that is necessary to ensure quality of data (Hashem et al., 2015). The aspects related to big data are storage systems, sensors and computing that poses challenges if not managed properly. The big data has hidden values that are dependent on freshness of data. The fourth challenge is analytical mechanism where big data processes huge amount of data within a limited period (Sun et al., 2016). The traditional database systems lack scalability and flexibility. The big data poses challenges when intermixed with traditional database. The fifth challenge is confidentiality of data, which poses the challenge of maintaining and handling of data (Hashem et al., 2015). The data are huge sets and hence service providers are unable to handle the data sets due to their limited capacity of managing data. The sixth challenge is energy management, which shows that big data in Cloud and Internet of Things absorbs high energy (Hassanalieragh et al., 2015). This cause effect on environment and economy perspective. The seventh challenge is scalability, which shows that big data must support current and future datasets that may change.

Intertwining of Big Data, Cloud and Internet of Things

This shows that big data poses challenges in Internet of Things and Cloud.  The challenges are several and they need focus and evaluation for future prospective (Sajid, Abbas & Saleem, 2016). However, there are several advantages of big data analytics in Cloud and Internet of Things (Liu et al., 2015). The advantages of big data in Internet of Things and Cloud are reduced cost, virtualization and instant access to infrastructure in cloud.

The big data is influencing organizations through use of Internet of Things and cloud and they are generating at high rates (Liu et al., 2015). The future of big data depends on Internet of Things and Cloud. This can be shown as follows. The Internet of Things is the future of digital world and it is nothing nut networks that are interconnected to provide network services for data (Wang & Ranjan, 2015). The cloud is a reliable technology that helps to provide infrastructure to the organization for storing data.

The big data and Internet of Things both provide services as follows. The Internet of Things provides connection of machines and sensors to be used and big data to enable the move of network from virtual world to real world. The smart and connected devices give perfect explanation of adopting big data and Internet of Things (Terzi, Terzi & Sagiroglu, 2015). This explains that Internet of Things and big data analytics consists of various Internet of Things data. The big data analytics has various levels and each level are as follows. The real time level is used for analysing huge data through the sensors using Greenplum and Hana (Sajid, Abbas & Saleem, 2016). The offline level is used for applications where requirement for response time is not high. The existing architecture/tools for this level are Scribe, Kafka, Timetunnel and Chukwa (Da Xu, He & Li, 2014). The memory level is used for data where the volume is smaller than the cluster having maximum memory. The existing tool for this level is MongoDB (Terzi, Terzi & Sagiroglu, 2015). The business intelligence level is used when the memory level is surpassed by scale using the tool data analysis plans. The massive level is used when data totally surpasses the business intelligence capacity and databases. The existing tool for this level is MapReduce.

The Internet of Things and Cloud has various applications in various industries and the applications are as follows. The first domain is transportation consisting of smart parking, driving through 3D assistance (Sajid, Abbas & Saleem, 2016). The second domain is Smart environments domain consisting of smart water supply, smart home and offices. The third domain is Healthcare domain consisting of health tracking and pharmaceutical products. The fourth domain is food sustainability and fifth domain is futuristic applications domain consisting of robot taxi and city information model (Hashem et al., 2015). There are challenges related to Internet of Things for future direction. The challenges are architecture, environment innovation, technical, hardware, privacy and security, standard, business, development and data processing including heterogeneous data, noisy data and massive-intensive data. The big data issue in Cloud is based on infrastructure provided by cloud for big data storage (Suciu et al., 2015). This issue varies from one organization to another organization. The large unstructured data generated are dealt through big data analytics however; data storage in cloud poses challenges as cloud provides various infrastructures (Perera et al., 2015). The one technology, which is currently being used and widely accepted, is fog-computing technology. The fog computing provides several benefits. The benefits are bringing data near to the user, creation of dense geographical distribution, true support to Internet of Things (Yi, Li & Li, 2015). There are various Internet of Things technologies are discussed in this report. These technologies are near field communication, radio frequency identification, and low energy Bluetooth and wireless, radio protocols, LTE-A and WiFi-Direct (Perera et al., 2015). These technologies correlated with big data and cloud on a big platform to provide large future opportunities. This correlation also poses challenges in the real world environment if not managed properly and carefully (Aly, Elmogy & Barakat, 2015). The big data is rapidly growing and to accommodate big data in Internet of Things and Cloud there are various technologies and techniques that will be discussed in further sections. The problem discussed in this report is how big data poses challenges in Internet of Things and cloud.

Applications of Big Data, Cloud and Internet of Things in Various Industries

The above are the concerns and problems of the topic discussed and the topic will be elaborated in further sections through prior researches and methodology (Aly, Elmogy & Barakat, 2015). The purpose of this report is to focus on the challenges of big data in Internet of Things and Cloud to evaluate various existing and developing technologies for Internet of Things. The report also focuses on the different applications of Internet of Things along with focus on big data and cloud in real world environment (Marjani et al., 2017). The report presents prior researches on big data challenges in Internet of Things and cloud to evaluate the technologies. The report provides issues of the challenges and the solution for these challenges with focus on future research. The report presents the methodologies through which the Internet of Things, big data and cloud are evaluated in this report. The methodologies used in the literature review section are described and compared to choose the best-suited methodology for this report. The report also presents tables and graphs to illustrate more about this topic and support the justifications made in this report.

The outline of this report is as follows. The first section is literature review on past and present technologies related to big data challenges in Internet of Things and Cloud. This section briefly describes the researches done on big data, Internet of Things and Cloud to identify the Internet of Things technology. The given section covers the major part of the report that provides brief details of the problems and challenges of the topic discussed in this report. The second section is methodology section and it contains past and current methodologies used in the literature review section. This section also consist comparison of methodologies from different perspectives such as simplicity, efficiency, cost and time saving, connectivity and feasibility. This section then provides the best methodology or combination of methodology to suit the purpose of this report based on several factors. The report then provides issues along with solutions for the future research perspective.

The challenges of Big Data in IoT and cloud have posed various difficulties in adoption of Big Data when used along with IoT. These challenges are important to solve due to data storage, data security and integrity of data related with Big Data. The Big Data poses problems such as huge and complex data and data are biased. There are several Internet of Things technologies which help to solve challenges of Big Data such as Hadoop and Spark. There are several methods that have been presented to tackle the challenges of Big Data and the most important method is use of Hadoop. The Big Data challenges in IoT and Cloud need to be solved for future purposes.

Challenges of Big Data in Internet of Things

This section focuses on the challenges of big data in IoT and Cloud computing through prior researches done on this topic. Big data is intertwined with IoT and Cloud to provide a better platform for managing data (Da Xu, He & Li, 2014). The IoT, Big data and Cloud provide better integration for every sector however, there are challenges with these integration. These challenges are focused in the following sections.

The attraction and interest of big data in every industry is increasing rapidly. The industries from social networks to multimedia to business transactions all has adopted or on a verge of adopting these technology. There are 4 Vs related big data, volume, variety, velocity and value that poses challenges for researchers (Ahmed et al., 2017). The storage of and processing of data is a challenge for every sector. The cloud computing is the recent technology that has emerged to solve the big data problems by providing cost effective data center to store huge data. However, there are concerns related to service qualities of cloud such as privacy of user and data security (Da Xu, He & Li, 2014). The security concern is the top most priority regarding the use of cloud. IoT is a platform for next-generation computing which is integrated in every sector from an individual’s life to an organization. This platform works in real-timed fashion in every sector and has large datasets that is generated over a particular time ((Ahmed et al., 2017). The prior researches shows that there are areas associated with big data security in cloud and IoT which needs to be looked upon for future researches (Liu et al., 2015). The areas are efficiency including communication and storage and computation time, security and scalability.

The various platforms big data platforms poses challenges in IoT and cloud and they are discussed as follows.

Challenges of Apache Hadoop: lack of encryption at network and storage level, unsuitable for small data, limited flexibility and high input-output overhead (Ahmed et al., 2017).

Challenges 1010data: data extraction, data loading and data transformation.

Challenges of Cloudera, a Hadoop based framework, are that there is no software and hardware systems of its own and relies on third parties.

hallenges of SAP-Hana are: all data must be read that is in a row even though a few columns of data are required for accessing.

Challenges of Hadoop Autonomy Vertica Enterprise security (HAVEn): a large database is generated by an increment in tenants where all operations related to release processes and lock holding are decelerated (Marjani et al., 2017).

Advantages of Big Data Analytics in Cloud and Internet of Things

Challenges of Hortonworks: it cannot minimize the node-groups in the cluster generated by the system.

Challenges of pivotal big data suite: it has several unresolved issues that hinders its adoption in industries.

Challenges of Infobright: Infobright optimizer cannot optimally answer all the queries (Ahmed et al., 2017).

Challenges of MapR: it has larger complexity as compared to Hadoop.

The big data challenges in IoT are as follows that arise due to introduction of big data in IoT. The challenges are as follows that are discussed here.

Privacy issue- The issue of privacy arises when there is any compromise of system for restoring sensitive data using tools of big data analytics. There is a problem regarding data mining where the privacy issue is high (Marjani et al., 2017). The users find it difficult to trust on usage of big data in IoT due lack of proper service level agreement for sensitive data misuse use such personal information. The other issues is security risk, related with IoT data, in heterogeneity of using devices and data generated such as data types, communication protocols and raw devices (Ahmed et al., 2017). The other challenges emerges due to generated data through IoT are as follows. The challenges are timely update of systems, managing identification of traffic patterns from suspicious and legitimate ones, interoperability and protocol convergence (Pfarr, Buckel & Winkelmann, 2014). However, true and accurate open ecosystem having standard APIs is required to prevent problems of reliability and interoperability. The other measures to tackle the privacy issue is protection of devices during peer-to-peer interaction (Da Xu, He & Li, 2014). The other measure is to hardcode the devices to protect it from common privacy and security issues.

Data mining issue- There are primary challenges for data mining where data exploration and information extraction poses challenges for big data in IoT (Liu et al., 2015). The challenges associated with processing of data and data mining are exhaustive data writes/reads. This provide challenges due to data exploration, heterogeneous communication, extraction processes and integration (Lee & Lee, 2015). The other issue is to extract actual and knowledgeable data from the large pool of data. The heterogeneity and size of big data also poses challenge in data mining. The large data sets has more complexity and ambiguity that leads to challenges and require additional steps for data mining (Terzi, Terzi & Sagiroglu, 2015). Hence, to minimize these challenges, parallel associated rule mining procedures and parallel k-means algorithm has been introduced.

Future of Big Data in Internet of Things and Cloud

Visualization issue- The visualization is difficult to achieve for big data, which is large and enormous. Visualization poses challenge for big data, to work on diverse and heterogeneous data. The visualization design for big data framework is difficult to achieve (Terzi, Terzi & Sagiroglu, 2015). The response time is also a big data challenge in IoT. The data are different and hence visualization of these data is also different. Thus, this poses a challenge to visualize data in IoT. The enormous parallelization of data is difficult in a scenario where big data is growing rapidly. The other issues are visual noise, information loss, large image observation, changing of image frequently and requirement of high performance (Pfarr, Buckel & Winkelmann, 2014). However, there are several guidelines introduced to minimize these issues and they are data awareness, data quality, meaningful results and interactive visualization tools.

Integration issue- The issue of integration of data is an issue that should be looked upon. These issue poses challenges as data is of different types (structured, semi-structured and unstructured) and generated from different sources (Liu et al., 2015). These shows that integration of data is difficult and complex. The challenges related with data integration are overlapping of similar data, increased performance, scalability and access of real-time data is enabled (Marjani et al., 2017). The other challenges are adjusting of unstructured and semi-structured data prior to integration and analysing. However, these issues can be addressed through text mining and other techniques of extracting.

The challenges of big data in cloud are given as follows.

Data quality issue- Big data poses challenge if data is not accurate and timely available (Balachandran & Prasad, 2017). If there is no guarantee of data quality when implemented through process of information management.

Data storage issue- The data are enormous and growing rapidly with requirement of big storage to adjust the storage of big data (Pfarr, Buckel & Winkelmann, 2014). This poses challenges for cloud computing to provide big storage capacity in competitive environment.

Privacy and security issue- The security and privacy are the major concern for big data. The organizations in every sector require to establish policies for security and privacy of data (Pfarr, Buckel & Winkelmann, 2014). However, this poses challenge as data are not limited to be suitable for the policies.

Hacking and several attacks- There is high risk of attacks and data breaches related with data in cloud (Litchfield & Althouse 2014). This is because even if only one part of cloud infrastructure is attacked, all the clients using the cloud infrastructure will be affected.

Internet of Things and Big Data Analytics

Delivery of service and billing issue- There is lack of proper service level agreement (SLA) given by the cloud provider for data storage that guarantees data scalability and availability (El-Seoud et al., 2017). The budget is also an issue as cloud provides on-demand service and it is difficult to assess whether it is justifiable or not.

Portability businesses and interoperability issue- The migration of services in and out should be smooth and without lock-in period (El-Seoud et al., 2017). This poses problem if proper migration is not done as it can lead to data loss.

Data availability and reliability issue- The proper monitoring of internal and third party tools, supervise usage, SLAs, robustness, business dependence and performance.

The below IoT technologies helps to solve the big data challenges in IoT and Cloud.         

The automatic identification and capture of data is allowed through radio frequency identification by using a reader, waves and a tag. There are three tags namely passive, active and semi-passive which provides identification for data in the system (Balachandran & Prasad, 2017). The applications of RFID are IT asset tracking, race timing, e-passport and transportation payments, logistics and supply chain, materials management, library and laundry management.

This technology consists of sensor-equipped devices that are spatially distributed to monitor and manage physical or environmental situations. The technology cooperate and coordinate with RFID technology to track all the movement and status of entities like temperature, location and others (Litchfield & Althouse, 2014). It provides low cost and low power for device usage. The applications of WSN are in military, health, environmental, home, commercial and industrial monitoring.

The communication and I/O is performed easily by software developers through middleware which is a software layer. This software layer is interposed between the software applications. It has features of hiding different technologies details that is fundamentally free IoT developers from using software services (Litchfield & Althouse, 2014). The example of middleware is Global Sensor Networks (GSN) that is an open source.

The cloud computing technology provides accessibility for on-demand access of configurable resources to a shared pool which can be conducted as SaaS or IaaS. This technology provides solution in back-end to manage large data streams and data processing for infinite number of humans and IoT devices in real-time scenario (El-Seoud et al., 2017). The applications of cloud computing are in social networking, big data analytics, chatbots, education, healthcare and banking industry.

Methods and Methodology for Evaluations of Big Data, Internet of Things and Cloud

The development of countless user-specific and industry-oriented application of IoT are facilitated by IoT. The devices integrated with IoT required to ensure proper action taken on received data in a timely manner (Litchfield & Althouse, 2014). The applications of this technology are in airline, pharmaceuticals, manufacturing, media and entertainment, insurance and business services.

The applications of IoT are in indistries such as logistics and transportation where the status of goods that are transported are monitored (Balachandran & Prasad, 2017). This monitoring of goods are done continuously to take appropriate actions in case of emergency. The other application is in healthcare where simultaneous monitoring and reporting, tracking of data and medical assistance in remote areas (Balachandran & Prasad, 2017). The other applications are in retail, smart home, supply chain and connected car and wearable devices.

The issues identified in this research paper are given as follows. The big data poses various challenges in Internet of Things and Cloud that are described in this research paper. The major challenge is related to security of data. The issues are related to privacy, security, data mining, visualization, integration, data quality, data storage, hacking and several attacks (Assunção et al., 2015). The other issues are service delivery and billing, portability businesses and interoperability, data availability and reliability. The big data challenges processing and platforms are also described in the research paper. These issues should be addressed with proper methods to minimize the challenges in Internet of Things and Cloud.

The paper will now focus on big data challenges in Internet of Things and Cloud along with Internet of Things technologies that can solve these challenges. The past and present methodologies to solve the problems of this research paper. They are discussed as follows.

Hadoop- The big data is collected and managed by Hadoop, which is managed by the Apache Software Foundation. It is an open source platform to manage big data. Hadoop provides data processing in parallelized form to allow quick computations and to hide latency (Bagheri & Shaltooki, 2015). The two main elements for Hadoop are Map Reduce engine and Hadoop Distributed File System (HDFS). The enormous data is stored in Hadoop Distributed File System. This data is reproduced to the client application at a very high bandwidth. On the other hand, MapReduce is a framework that is used for data processing in a more distributed way via several machines. Hadoop handles 3Vs related to big data, variety, volume and velocity (Jaseena & David, 2014). Hadoop deals with big data variety by providing storage of every type of data whether structured or unstructured. Hadoop handles volume of data by scaling out to provide more storage. Hadoop manages velocity of big data by loading raw data in the Information System and after that it can be viewed as per need (Yin & Kaynak, 2015). The flexibility of the system helps to smoothly load data without any congestion and changing data can also be accommodated with easy integration.

Future Research Perspective

Map Reduce- Map Reduce is defined as a paradigm for broad programming. The original actions were parallel execution, load balancing, data manipulation and fault tolerance (Chen & Zhang, 2014). The Map Reduce is so called because it has two abilities from that of existing functional computer aspects and languages: Map and then reduce. The Map Reduce is a framework that helps to collect data and gather them from records with a common key and then together joins them. This shows that for each different key that is produced acquire formation of single group (Wu et al., 2014). The Map Reduce is considered as a technology however, it is only an algorithm that provides how the data will fit into the system. The Map Reduce being just an algorithm has limited scope to manage big data however, it can be utilized for best to manage data if combined with other technologies. The Map Reduce is used in two phases to solve big data challenges and they are Map phase and Reduce phase (Reyes-Ortiz, Oneto & Anguita, 2015). The map phase helps to do functional operation on datasets to emit the key which is mapped and generate pairs for the output of this phase. The reduce phase helps to collect data from nodes and combines them in such a way that it comes out as expected output.

Hive- Hive can be determined as an essential part of Hadoop system that can be viewed as a principle aspect for the data warehouse in the system (Wang et al., 2016). The tools that are already deployed for data warehouse in the system are not able to adjust in the events where accessibility of data is everywhere and sometimes operated privately (Babiceanu & Seker, 2016).). Map Reduce provides tracking of reusable code characteristics that is difficult from business prospective (Zheng et al., 2015). On the other hand, Hive is a technology that cannot treat with transaction and applications for real-time analysis due to some complicated technique. Hive is used in conjunction with Hadoop and Map Reduce where several challenges of big data is overcome by this technology (Bagheri & Shaltooki, 2015). It has three main function and they are running of data queries, summarizing of data and big data analytics.

Mahout- This technology is built on an Apache library which is an open-source and able to scale up and down as per the requirement. This technology also helps to manage huge volume of data (Suthaharan, 2014). There are different segments that rely upon three particular machine learning aspects on which Mahout currently operates. These machine learning aspects are collaborative, clustering and classification. It is a library of various scalable algorithms for machine-learning that is implemented on top of Hadoop (Wang, 2016). Mahout is a recently introduced technology that helps to provide better integration for big data challenges. The machine learning technology helps to analyse data that are huge and repetitive, flowing in Internet of Things (Allen, 2017). This technology has already adopted by some sectors such as social media who are enjoying the leverage of this technology. Machine learning technology helps to analyse huge data and separates the unmatched data which is not as per the norms of data.

GFS- GFS is developed by Google Inc. and it is defined as distributed file system. The enhancement of GFS is for Google’s central data storage and requirements for usage that can enable huge quantities of data which requires recalling (Kim, Trimi & Chung, 2014). GFS technology is used for various purposes and they are scalability, performance, availability and reliability (Suthaharan, 2014). These purposes of the distributed file system is manipulated through technological environment and application workloads.

Thus, above are the methods and these can be used for to solve big data challenges in Internet of Things.

The methodologies are compared in terms of aspects that are described below.

• Hadoop is efficient, complex to use, can be extended to other applications as well. It is time saving and cost saving with flexibility to use (Zheng et al., 2015). However, issues are complexity and availability.
• Map Reduce is quite efficient, simple to use that can be extended to other applications also. It is time saving and costly with flexibility to use. However, issues are not suitable for small data and speed is slow.
• Hive is efficient, quite simple to use that can be extended to other application also. It is time saving and cot saving with flexibility in its structure (Cevher, Becker & Schmidt, 2014). However, it cannot deal with real-time analysis.
• Mahout is efficient, complex to use that ca be extended to other applications as well. It is also time saving and cost saving with flexibility to use. However, it cannot be applied on theoretical problems.
• GFS is efficient, simple to use that can be extended to applications. It is time saving and cost saving with flexibility. However, issues are cannot write randomly and not suitable for small sized data.

Therefore, from above points it can be shown that Hadoop along with Map Reduce, Hive and Mahout is the best methodology for big data challenges in Internet of Things and Cloud.

The big data, Internet of Things and Cloud are a big boon for every industry, if used conjointly. The future of these three technologies are great and it can have major impact on every sector. The big data is increasingly adopted by the organizations and its adoption has generated the need to adopt cloud computing and Internet of Things (Kim, Trimi & Chung, 2014). The Internet of Things, big data and cloud will transform businesses where they will be able to extract valuable data from pool of data that can maximise their business (Kim, Trimi & Chung, 2014). The Healthcare industry and banking industry are the other two major sectors which will be getting benefit from big data, Internet of Things and cloud computing. Healthcare industry will use these technologies to improve patients care and make them safer and secure in the healthcare environment (Wang et al., 2016). The banking sector will also be able to improve their performance by making a safer environment for customers so that they feel secure. The payment and transaction will become safer helping bank employees and customers to transact or pay securely without any fear of losing sensitive data due to any attack such as hacking or data breach (Skarmeta, Cano & Iera, 2015). The big data, Internet of Things and cloud will not only affect the industries but it will also help people to use for their individual purposes to do basic functions and operations (Durgude & Yalij, 2015). The accurate real-time analysis which I the most important stage in the current scenario for every industry and an individual will also be achieved due to these three technologies use.

The advantages and disadvantages of big data, Internet and Things and Cloud computing, with methodologies used, are given as follows.

Advantages

• Cost reduction- The cost will be drastically reduced with the use of these three technologies and this will enhance more frequent adoption and deployment of these technologies in various sectors (Bossé & Solaiman, 2016).
• Availability- The big data, Internet of Things and cloud are readily available to be used for services in any industry whether on-demand or on premise (Xu et al., 2015). The technologies are timey update automatically which also reduce manual work for every industry.
• Scalability and elasticity- The big data is scalable and has property of elasticity which expands out and expands in to adjust big data in the storage system to be available for every industry when required.
• Increased storage capacity- The storage can be increased as per the requirement of data which helps to store enormous data that are generated on a daily basis (Skarmeta, Cano & Iera, 2015). These technologies has capacity to increase their storage as maximum as it can.
• Fraud detection- These technologies when used conjointly, detect frauds easily and eliminate risks by taking proper steps and also helps to pre-plan for these risks in case of disasters.
• On-demand service- the service can be used on-demand which helps to minimize the concern of having physical data storage systems and provide more benefits than traditional data storage systems (Zheng et al., 2015).

Disadvantages

• Not compatible sometimes- The technologies are sometimes not compatible to benefit the organizations (Aly, Elmogy & Barakat, 2015). Sometimes poses challenges to manage these technologies together.
• Possible failure- These technologies can sometimes fail if not properly managed and maintained (Bossé & Solaiman, 2016). An expert management and maintenance is required where there will be no chance of failure.
• Privacy and security- The privacy and security are the major concerns that are important part of these technologies (Bossé & Solaiman, 2016). The privacy and security should be taken into account as these are the major aspect which can lead to failure of businesses in any industry.

Conclusion

Therefore, above discussions it can be concluded that, there are big data challenges in Internet of Things and Cloud. The big data challenges are given in this research paper to analyse its impact in Internet of Things. There are various challenges described in this research paper that are privacy and security issues, data mining issues and data integration issues and data storage issues. This paper discusses various technologies associated with the big data, cloud and Internet of Things that are important for the challenges to overcome. There are various applications of these technologies when conjoint together. The applications are discussed in this report. The report shows that there are challenges which needs to be looked upon with proper emphasis on each technology and tools. The methodologies to solve these challenges has been discussed in this report to propose a solution for the big data challenges. The big data challenges in Internet of Things and Cloud shows that the challenges related to Internet of Things and Cloud that are essential to be taken into account. The possible strategies in the research paper shows that there are various technologies that have been previously deployed and are on a verge of deploying technologies. The methodologies are helpful to support big data challenges in Internet of Things. The best methodologies are combination of Hadoop along with Map Reduce, Hive and Mahout is necessary to minimize big data challenges in Internet of Things and Cloud. This combination is the possible and best solution for the present and future scenario with adding up of more advanced and new technologies. These methodologies has several advantages which are beneficial for big data challenges in Internet of Things and Cloud. These methodologies provide various benefits such as reduction in cost for future opportunities, scalability and flexibility of methodology to improve big data challenges in Internet of Things and Cloud. However, there are advantages and disadvantages of these three technologies that is essential to be taken into account before and after adopting these technologies. The several drawback of big data challenges in Internet of Things are compatibility issues and power failure in events of disruption. Hence, it can be concluded that the challenges can be minimized with proper use of these methodologies. The future research shows that if these technologies maintained properly then it can have major boost in performance of every industry using combination of big data, Internet of Things and Cloud. Therefore, big data challenges in Internet of Things and Cloud are the factors that needs central attention and valuable strategies to manage and maintain the technologies.

References

Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2017). The role of big data analytics in Internet of Things. Computer Networks, 129, 459-471.

Allen, T. (2017). SAPVoice: How To Solve IoT’s Big Data Challenge With Machine Learning. Forbes. Retrieved 13 April 2018, from https://www.forbes.com/sites/sap/2017/02/02/how-to-solve-iots-big-data-challenge-with-machine-learning/#7f1d64c723d5

Aly, H., Elmogy, M., & Barakat, S. (2015). Big Data on Internet of Things: Applications, Architecture, Technologies, Techniques, and Future Directions. Int. J. Comput. Sci. Eng, 4, 300-313.

Aly, H., Elmogy, M., & Barakat, S. (2015). Big Data on Internet of Things: Applications, Architecture, Technologies, Techniques, and Future Directions. Int. J. Comput. Sci. Eng, 4, 300-313.

Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., & Buyya, R. (2015). Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79, 3-15.

Babiceanu, R. F., & Seker, R. (2016). Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Computers in Industry, 81, 128-137.

Bagheri, H., & Shaltooki, A. A. (2015). Big Data: challenges, opportunities and Cloud based solutions. International Journal of Electrical and Computer Engineering, 5(2), 340.

Bagheri, H., & Shaltooki, A. A. (2015). Big Data: challenges, opportunities and Cloud based solutions. International Journal of Electrical and Computer Engineering, 5(2), 340

alachandran, B. M., & Prasad, S. (2017). Challenges and Benefits of Deploying Big Data Analytics in the Cloud for Business Intelligence. Procedia Computer Science, 112, 1112-1122.

Bossé, É. & Solaiman, B. (2016). Information fusion and analytics for big data and IoT. Artech House.

Cevher, V., Becker, S., & Schmidt, M. (2014). Convex optimization for big data: Scalable, randomized, and parallel algorithms for big data analytics. IEEE Signal Processing Magazine, 31(5), 32-43.

Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.

Da Xu, L., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on industrial informatics, 10(4), 2233-2243.

Da Xu, L., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on industrial informatics, 10(4), 2233-2243.

Díaz, M., Martín, C., & Rubio, B. (2016). State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. Journal of Network and Computer Applications, 67, 99-117.

Durgude, D. M., & Yalij, N. S. (2015). Big Data Analysis: Challenges and Solutions. International Journal of Scientific Research and Management, 3(2).

El-Seoud, S. A., El-Sofany, H. F., Abdelfattah, M. A. F., & Mohamed, R. (2017). Big Data and Cloud Computing: Trends and Challenges. International Journal of Interactive Mobile Technologies (iJIM), 11(2), 34-52.

Fazio, M., Celesti, A., Puliafito, A., & Villari, M. (2015). Big data storage in the cloud for smart environment monitoring. Procedia Computer Science, 52, 500-506.

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115.

Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., … & Andreescu, S. (2015, June). Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: Opportunities and challenges. In Services Computing (SCC), 2015 IEEE International Conference on (pp. 285-292). IEEE.

Jaseena, K. U., & David, J. M. (2014). Issues, challenges, and solutions: Big data mining. Computer Science & Information Technology (CS & IT), 131-140.

Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85.

Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440.

Litchfield, A., & Althouse, J. (2014). A systematic review of cloud computing, big data and databases on the cloud.

Liu, C., Yang, C., Zhang, X., & Chen, J. (2015). External integrity verification for outsourced big data in cloud and IoT: A big picture. Future Generation Computer Systems, 49, 58-67.

iu, C., Yang, C., Zhang, X., & Chen, J. (2015). External integrity verification for outsourced big data in cloud and IoT: A big picture. Future Generation Computer Systems, 49, 58-67.

Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I. (2017). Big IoT data analytics: Architecture, opportunities, and open research challenges. IEEE Access, 5, 5247-5261.

Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I. (2017). Big IoT data analytics: Architecture, opportunities, and open research challenges. IEEE Access, 5, 5247-5261.

Perera, C., Ranjan, R., Wang, L., Khan, S. U., & Zomaya, A. Y. (2015). Big data privacy in the internet of things era. IT Professional, 17(3), 32-39.

Pfarr, F., Buckel, T., & Winkelmann, A. (2014, January). Cloud Computing Data Protection–A Literature Review and Analysis. In System Sciences (HICSS), 2014 47th Hawaii International Conference on (pp. 5018-5027). IEEE.

eyes-Ortiz, J. L., Oneto, L., & Anguita, D. (2015). Big data analytics in the cloud: Spark on hadoop vs mpi/openmp on beowulf. Procedia Computer Science,

3, 121-130.

ajid, A., Abbas, H., & Saleem, K. (2016). Cloud-assisted iot-based scada systems security: A review of the state of the art and future challenges. IEEE Access, 4, 1375-1384.

Skarmeta, A. F., Cano, M. V. M., & Iera, A. (2015). Guest Editorial: Smart Things, Big Data Technology and Ubiquitous Computing solutions for the future Internet of Things. JoWUA, 6(1), 1-3.

Suciu, G., Suciu, V., Martian, A., Craciunescu, R., Vulpe, A., Marcu, I., … & Fratu, O. (2015). Big data, internet of things and cloud convergence–an architecture for secure e-health applications. Journal of medical systems, 39(11), 141.

Sun, Y., Song, H., Jara, A. J., & Bie, R. (2016). Internet of things and big data analytics for smart and connected communities. IEEE Access, 4, 766-773.

Suthaharan, S. (2014). Big data classification: Problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Performance Evaluation Review, 41(4), 70-73.

Terzi, D. S., Terzi, R., & Sagiroglu, S. (2015, December). A survey on security and privacy issues in big data. In Internet Technology and Secured Transactions (ICITST), 2015 10th International Conference for (pp. 202-207). IEEE.

Terzi, D. S., Terzi, R., & Sagiroglu, S. (2015, December). A survey on security and privacy issues in big data. In Internet Technology and Secured Transactions (ICITST), 2015 10th International Conference for (pp. 202-207). IEEE.

Wang, H., Xu, Z., Fujita, H., & Liu, S. (2016). Towards felicitous decision making: An overview on challenges and trends of Big Data. Information Sciences, 367, 747-765.

Wang, L. (2016). Machine learning in big data. International Journal of Advances in Applied Sciences, 4(4), 117-123.

Wang, L., & Ranjan, R. (2015). Processing distributed internet of things data in clouds. IEEE Cloud Computing, 2(1), 76-80.

Wang, Y., Wei, J., Srivatsa, M., Duan, Y., & Du, W. (2016). IntegrityMR: Exploring Result Integrity Assurance Solutions for Big Data Computing Applications. International Journal of Networked and Distributed Computing, 4(2), 116-126.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.

Xu, X., Sheng, Q. Z., Zhang, L. J., Fan, Y., & Dustdar, S. (2015). From big data to big service. Computer, 48(7), 80-83.

Yi, S., Li, C., & Li, Q. (2015, June). A survey of fog computing: concepts, applications and issues. In Proceedings of the 2015 Workshop on Mobile Big Data (pp. 37-42). ACM.

Yin, S., & Kaynak, O. (2015). Big data for modern industry: challenges and trends [point of view]. Proceedings of the IEEE, 103(2), 143-146.

Zheng, Z., Wang, P., Liu, J., & Sun, S. (2015). Real-time big data processing framework: challenges and solutions. Applied Mathematics & Information Sciences,