Data Management in Companies

Discuss about the Issues in Big Data Analytics.

Data and information have been the most important assets for the company in the market. Data can be used in different field that might help in maintaining the growth of the company. Data are important orders to carry out daily activities in an organization. Data can be collected from various sources that are available for the company in the market. The storage of data and information has been a huge problem for the companies in the market.  There are the huge amount of data in company related to their daily activities and the growth of the company in last years.  Therefore, such a huge amount of data and information can be managed by use of the concept of Big Data Analytics. This concept helps in the data management in the companies.  The database of the company can be handled by big data analytics. The huge usage of the internet has increased the amount of data and information in the companies. The use of big data has been maintaining data storage of the companies in the database. However, with all the benefits of big data, there are various privacy and security issues prevailing in the big data concept. 

These issues have been prevailing in big data concept and creating problems for the security of data and information in the companies. However, According to a report by International Data Corporation (IDC), a research company claims that between 2012 and 2020, the amount of information in the digital universe will grow by 35 trillion gigabytes. The emergence of cloud computing and social media have increased sources of data and information in the market.  As of December 2015, Facebook has an average of 1.04 billion daily active users, 934 million mobile daily active users, available in 70 languages, 205 billion photos uploaded every day 30 billion pieces of content, 2.7 billion likes, and comments are being posted and 130 average number of friends per Facebook user.

This report identifies the issues prevailing in the data implementation in the companies. This report has focused on the security and privacy issues in big data is creating the major problem in the companies.  The data breach and data loss have been described in the report.  This report outlines about six major big data issues in the market.  Twelve journals have been properly analyzed in order to analysis these issues related to big data. Recent works in these issues have been discussed in the next part. The use of proper methodology for minimizing the issues in big data have been provided in a report.  These methodologies might help in maintaining and monitoring the issues in big data. The use of various algorithms and coding for mitigating these issues have been provided in the report.  There are some recommendations provide in the report that might help in minimizing threats and security issues in big data analytics.

Security and Privacy Issues in Big Data

There are various security issues in big data analytics. However, six major security issues have been discussed below:

Application of tech

This journal describes security issues in big data related to the cloud computing. The main focus of the paper is on security and privacy issues in cloud computing using big data concept. Cloud computing has been suffering from cyber-attacks and online threats including virus attacks and malicious intruders.

About technology

 Big data is stored in the online database of the cloud computing that helps in minimizing the complexity of hardware database of storing such a huge amount of data and information. Data privacy has an important concern for the companies in the market. Several private data of the companies are stored in the cloud storage services. 

Research issues

The challenges are categorized into several categories including network level, authentication level, data level and generic types. In network level, the security of the data with the intrusion in the network connectivity and communication. The authentication methods in the including administrative rights and authentication of applications.

Che, D., Safran, M., & Peng, Z. (2013, April). From big data to big data mining: challenges, issues, and opportunities. In International Conference on Database Systems for Advanced Applications (pp. 1-15). Springer, Berlin, Heidelberg.

Application of tech

This paper discusses  security challenges in big data mining and relation with the cloud.  The use of the cloud computing for maintaining the data storage and others services provided to the company. 

About technology

Big data has been a crucial part of the cloud computing for storage purpose of big data over the internet.  Therefore, there are several security issues discussed in the paper. Data Overwrite has been one of the major problems in the cloud services.

Research issues

Data and information stored over the internet might be overwritten during the write mode. This might create problems for the companies and users of the cloud. In the data levels, it deals with the data integrity and availability including logging and nodes. The generic security types have been related to the in general security issues about the data loss and integrity.

This paper deals with management issue of big data in a database.  There is a huge amount of data and information stored in the database.  Therefore, management of the data and information needs to be managed in the proper manner. This creates the problem for the data base administrator to manage such a huge amount of big data.

Emergence of Cloud Computing and Social Media

 In many cases, data and information are jumbled up and overwrite occurs. Data can be breached during the transfer of data from sender to receiver that involves distributed data, distributed nodes and internode communication.

As the authentication level, the security of the data deals with the encryption and decryption of data and information at both sender and receiver ends.  This creates wrong information for a particular data and information. This has been a major issue discussed in this paper. 

This paper deals with the challenges in big data related to geographical intent.  Data collected from various places on the environment varies with their quality and size. Social media data collection has been a major source of data and information.

Millions of data and information are stored in the data base of big data analysis that creates a major issue.  The severity protocol for big data analytics can be hacked or attacked in order to intrude into the data base of big data. 

This creates a huge amount of problems s all the data and infestation related to the different companies and users can be leaked in the market. This might create a huge financial loss to users and companies in the market.

Issue 3- Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: issues, challenges, tools and good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on (pp. 404-409). IEEE.

This paper describes the data access issues in big data databases. In several cases, the access of the data from databases becomes difficult for making a proper decision in the system.  Data needs to be store properly in the database.  Therefore, in case the database is failed or damaged, access to the database become difficult for the users.

Data privacy has an important concern for the companies in the market. Several private data of the companies are stored in the cloud storage services.  The challenges are categorized into several categories including network level, authentication level, data level and generic types.

Sharing of data from one party to another might create problems in big data as cyber-attacks can happen during the data transfer. Therefore, sharing data and information about the organization threatens the environment if the organization.

This paper deals with the storage issues faced by big data. In some cases, storage might be available for the huge amount of data and information in a database.  Data management problem has been the main problem for big data analysis for the system.  The use of big data has been initiated in a market that needs to be maintained and managed properly.  Therefore, storing such a huge amount of data and information.

Six Major Big Data Issues in the Market

This paper discusses data visualization in big data. This paper focused on the scientific data lifecycle management as this is an important part of maintaining different big data in a database. Big data science helps in maintaining clear indication in the storage of the data and information. 

The use of big data aims the science has helped in providing several duty of the team in order to maintain the data base if the scientific data. Therefore, in the case of the working implementation of the database analysis.

The scientific data might get vanished or corrupted in the database.  This might create a major problem in the organization in order to maintain a proper database for a company.

This paper has made a statement of the security issues that arise from the implementation of big data in approaches. There is an importance in maintaining the use of big data in databases. Due to this, the data can be monitored, collected and stored in their own personal records and can be used. However, there are various requirements that are in place due to this which presents many security issues in it. This is of increasing concern to the implementers of big data processes in their technologies such that they can address the issues available with big data implementations.

The use of big data systems is also being used in case of cloud computing systems as well. As a result, companies are also applying the options for scalability on their cloud infrastructure. The cloud computing is a technology by which the applications can be hosted on virtual servers which helps in reducing the need for physical storage spaces. The use of big data has been adopted in the cloud-hosted servers such that the information can be applied to the virtual servers in a quick way. This will help in increasing the effectiveness of the cloud structures and thus will increase the efficiency of the company involved.

This paper is mainly concerned with challenges faced by the implementation of big data tools. These issues are the mainly the security breaches. As companies now have access to a wide range of information pertaining to their requirements, the need to secure the information is also to be considered. As a result, the hackers are targeting companies with data. This causes the companies to get a pre-requisite for security policies. This paper is worried about the utilization of enormous information and its applications which can help in the utilization of different related assets with the goal that it can exceed expectations in their administrations. All the data can be utilized to produce bits of knowledge on the conduct of the information and in this manner get important experiences from them

Methodologies to Minimize Big Data Issues

The use of big data in analytics and information is a major breakthrough that has revolutionized the technology industry. The smart analytics in big data technologies have been mentioning various interventions in the market. Several private data of the companies are stored in the cloud storage services.  The challenges are categorized into several categories including network level, authentication level, data level and generic types. Verification of information and client should be done in the framework that may help in keeping up the security of data in the database. The key administration of the database has been giving a key approach to the insurance of information from digital assaults and interlopers. Different enormous information bunches used to ensure their information by executing key in their nearby plates and memory.

This paper is mainly concerned with the opportunities and challenges that are present with the adoption of big data. The opportunities include the development of a new revenue stream. Companies can collect data which can then be used for third party advertisements. This will help in increasing the engagement made and thus, advertisers will be benefitted. This has helped different organizations to use these prospects and hence get a general wage stream from these investigation. Numerous new organizations have developed which are included with the observing of investigation and tending to them in organizations. The advanced model of business has come into the prospect by using this framework. This paper is basically worried about the nearness of innovative issues with big data. The organizations likewise need to address them in their answers with the goal that they can shield their data from ruptures or security hacks

According to the literature provided in this paper, use of big data has been involved in addressing many problems. The smart cities are another implementation of the technological advances from big data. Big data analysis has been advanced analytics techniques for large data sets by helping in hiding patterns in the database. The use of the big data has been helping in maintaining several phases of the data acquisition and integration. The difficulties in the big data have been expanding in a large scale based on mixed methods. However, collected data can be both structured and unstructured form. Therefore, unstructured create difficulties in accessing them from the database (Kanika, A., & Khan, 2018). Therefore, big data analysis becomes more complex in the case of unstructured data. Incomplete and unstructured data creates uncertainty in the data analysis that needs to be managed at the time of data analysis. However, incomplete data refers to missing of some data bits from original data.

Recommendations for Minimizing Big Data Threats

The heterogeneous behaviour of the data increases the complexity of the data management. The technical specifications are utilized in collecting the data associated with the several parameters surrounding the aspects. This paper is concerned with the use of big data and its applications which can help in the use of various associated resources so that it can excel in their services. All the information can be used to generate insights on the behaviour of the data and thus get meaningful insights from them (Katal, Wazid &Goudar, 2013). As a result, this will help in addressing the solutions of a company for better capturing of the engagement. Management of data in the database has been a major problem in the big data analysis.

The searching query of the database becomes a failure in this case. As the size of the data increases, the analyzing time gets increased in the processing. However, big data can be able to manage the huge number of data at a time that can provide benefits to the companies and users.  The clustering property of the big data might not able to maintain the volume of data stored in the database (Khan et al., 2014).  The heterogeneous behaviour of the database increases both time and space complexity in the database.  Fetching of data from the database gets increased due to the heterogeneous functionality of data in the database.  The tables and rows in the database become different in case of heterogeneous data allocation. The use of big data in business processes has been in place since the emergence of this technology (Kitchin, 2013).

This has helped various companies to utilize these prospects and thus get a general income stream from this analytics. Many new companies have emerged which are involved with the monitoring of analytics and addressing them in businesses. The digital model of business has come into the prospect by utilizing this system. This paper is mainly concerned with the presence of technological problems with big data. The companies also need to address them in their solutions so that they can safeguard their information from breaches or security hacks (L’heureux et al., 2017). Large amounts of data pose the same problem as storing large amounts of gold bullion. This resource discusses the context of big data and the reason behind its application. Different types of data have different storage needs. That makes it difficult to integrate them sometimes, in the same way, that it can be difficult to get two Excel reports to integrate into a pivot table.

According to this paper, the integration of big data in cloud computing is being involved in various problems that are to be referenced for improving the strengths of the business. The opportunities this can provide is the increase in scalability of the company which leads to the development of much higher level infrastructure (Lee, Kao & Yang, 2014). This will help in real-time monitoring of the system and changes in records to meet the demands. This paper is mainly concerned with challenges of big data technologies. The main challenges are the need for security enhancements and need for constant monitoring of the information. This is the main challenge in case of big data technologies which has to be addressed for efficient implementations of big data. This paper discusses the involvement of big data in public sector. As public sector consists of a large amount of customer base, the use of big data can help in increasing the advertisement standards. The public sector has been maintaining big data in the market for maintaining a large amount of data and information. The public sector has been creating the problem in managing data from various sources. This paper concerns its discussion about the use of it in case of public sector organizations. Various implementation in the public sectors has been maintaining use of big data in the market (Kitchin, 2013).  The use of big data has been creating problems related to the public relations in the market.

Various public policies have been failed in maintaining services. This paper discusses the advantages of utilizing the concept of big data on social networking prospects. By utilizing context of big data and its solutions, monitoring of data can be effectively done which will help advertising solutions to get maximum effectiveness. Big data challenges have been involved with the prospects of collecting data and monitoring them to get insights. As a result, this will be utilized in company solutions such that it can increase business. The challenges present with the spatial analytics are also to be referenced. This paper is focused on the use of big data on machine learning. This requires a pre-requisite for implementing changing algorithms such that main system can be able to adapt to the changes in the analytical behaviours. There are various challenges which include that administrators must monitor the system and thus helps in referencing them. The machine learning with the help of big data has been creating the problem in the analytics.  The use of various elements in big data analytics has been helping in the machine learning. The eXtensible Access Control Markup language (XACML) might help in maintaining a proper basis for functionality on big data analysis.  Therefore, the use of the additional research in elements of big data analysis to have been maintained in the methodology. Therefore, for supporting secure of data and information in the data processing SDI needs to have a corresponding Access Control an Accounting Infrastructure (ACAI) ensures normal infrastructure operations.  The use of different identification and verification of the access control helps n providing data security to the users.  The use of the biometric technology can be important in the security model that might help in creating a verification process before getting access to the account.  However, moving to the Open Access might be difficult for the security model as some partial change is required in order to maintain the security level of the data and information in the account (Kanika & Khan 2018).  The use of SDI security mode in the future might help in providing a better approach to the maintenance of the data and information over the internet using big data analysis.  Therefore, the required ACAI needs to have following features that might help in maintaining the security and minimizing the complexity of the security component of the device. Thus, this will be used in organization arrangements with the end goal that it can expand a business. The difficulties give the spatial examination are additionally to be referenced. This paper is centred around utilization of big data on machine learning. This requires a pre-imperative for actualizing changing calculations with the end goal that principle framework can have the capacity to adjust to the adjustments in the investigative practices. There are different difficulties which incorporate that managers must screen the framework and along these lines helps in referencing them. The machine learning with the assistance of big data has been making the issue in the investigation. The utilization of different components in big data investigation have been helping in the machine learning.

Authentication of data and user needs to be done in the system that might help in maintaining the security of information in the database. The key management of the database has been providing a key approach ti with the protection of data from cyber-attacks and intruders (Katal, Wazid &Goudar, 2013). The data management has been an issues in the big data analytics.  Huge amount of data information have been stored in the database of the organization.   Therefore, managing such huge amount of data has been a difficult task for the system.  Therefore, this has been creating a problem in big data analytics.  The journals have been taken related to the issues in the hug data. A proper methodology has been provided in report for mitigating the issues in big data analytics.  A proper security model has been proposed in the report that might help in maintaining the security of the data and information of the organizations.  The use of data analytics in report has been maintaining security and privacy of the data an information in the data bases.  All factors that are discussed related to issues in big data analysis have been properly analyzed in the section that might be in proposing a security model for big data analysis. Organization and uses of big data have been maintaining a proper data security and privacy over the internet. Various big data clusters used to protect their data by implementing key in their local disks and memory. The use of secured communication with the data base helps in creating a positive image in the mind of a user for using the big data analysis. Therefore, the use of SSL/TLS has been implemented in providing security to the networks of big data.

The issues in big data have been discussed with the help of twelve journals in the above sections.  Therefore, in this part, a security model based on the cloud security has been proposed. Therefore, ensuring data security in big data analysis and applications in a various sector as discussed in the above sections (Han et al. 2018). All the factors that are discussed related to issues in the big data analysis have been properly analyzed in the section that might be in proposing a security model for big data analysis. Organization and uses of big data have been maintaining a proper data security and privacy over the internet.  The SDI architecture for the e-science model contains several layers mentioned below:

Layer D1: Network infrastructure layer can be represented by general purpose Internet Infrastructure and other network infrastructure.

Layer D2: Computer resources and data centers.

Layer D3: Infrastructure virtualization layer has been represented by Cloud and Grid infrastructure services for supporting middleware scientific platforms deployment operation.

Layer D4: Instruments and scientific platforms for various researches.

Layer D5: Federation and Policy layer including federation infrastructure components involving policies of other groups and support facility.

Layer D6: Scientific applications and user portals/clients

There can be three cross-layer planes including Operational Support and Management System, Security plane and Metadata Life cycle management. However, dynamic character of SDI has been distributed by the multi-faceted communities that can be guaranteed by the dedication of the layers: D3- Infrastructure Virtualization layer uses various modern cloud technologies, D5- Federation and policy layer for incorporating related to the infrastructural management and accessing technologies (Huda et al. 2018). Therefore, introducing the Federation and Policy layers helps in building and managing complex SDIs by allowing independently managed infrastructures for sharing resources and support organizational cooperation. Big data challenges have been included with the possibilities of gathering information and observing them to get experiences.

This figure focuses on the complexity of the security trust provided by use of big data analysis of the direct user an organization of big data.  However, in general case, the various provider of big data security protocols in the market can assure about the security of the data and information on the internet.  Data-centric models offer security to the data and information stored on the internet that can be used by the provider of e-SDI environment. This model might help in minimizing the threats and issues in big data analysis.  This proposed security model includes several policy binding policies in order to maintain the security framework for gaining data access. Therefore, the data access problem in big data can be minimized (Wani & Jabin 2018).

• Empowerment of the researcher in order to maintain the data processing for shared facilities of large data centres for guaranteeing data and information security.
• Motivating researchers for sharing their research environment and schedule with other researchers for developing the language in a better manner. The use of the tools and techniques required for the development of language might help in maintaining the customized infrastructures for allowing other researchers to work on the existing data sets.
• Protecting data and information by using different data policies and ownership linkage produced in the data archiving (Sivarajah et al. 2017). However, these technologies might be helping in ensuring the protection of the data and information over the technologies ensuring data readability and accessibility with the change in the technologies.

The main goal of the enterprise for supporting the enterprise workflow procedures related to the processes monitoring and processing. Cloud technologies help in simplifying production of infrastructure and another network (L’heureux et al. 2017). The infrastructure segments of the include IaaS (VR3-VR5) and PaaS (VR6, VR7), separate virtualized resources and services (VR1, VR2) and interacting campuses A and B and interconnecting them with other interacting infrastructure. 

There are various algorithms that help in maintaining issues in big data. These algorithms help in minimizing the issues related to security of data and information in big data. Some of these algorithms are discussed below:

MAXMIN Ant System algorithm

Max-Min Ant system is an optimization algorithm that has been inspired by the behaviour of real ants. These algorithms have been popular and effective for solving the NP-hard combinatorial problem including travelling salesman problem. Efficient Ant Colony Optimization techniques are the key to meet the scalability and performance of big data analytics in the system.  The parallel problems of big data analytics can be mitigated by this algorithm. Therefore, MMAS algorithm in MapReduce framework and helps in presenting MRMMAS for providing a method to be applicable in mitigating the issues in big data (Yang et al. 2017). However, this algorithm is simple and flexible in nature. MapReduce helps on minimizing the function of MRMMAS. This algorithm is based on the iteration property of the variables used in algorithm. Therefore, for decreasing the production of identical solutions in the market.  New materials are important for the organization.

τij ← (1 − ρ)τij + ?τ best ij

with the constraint that there exists τmin and τmax such that τmin ≤ τij ≤ τmax and where ?τ best ij is given by:

?τ best ij = { 1/Lbest if (i, j) belongs to the shortest tour; 0 otherwise

The values of τmin and τmax are typically obtained empirically and are influenced by the problem in consideration. The values are carefully chosen in order to avoid stagnation of the search, but also to ensure good solutions are found in an efficient manner (Shi et al. 2018).

The Ant Colony System algorithm

The ACS algorithm varies from the AS by introducing local pheromone update in addition to the update performed at the end pf the solution.

Local updating encourages exploration of the search space by decreasing pheromone levels on traversed edges. Each ant applies the following update to the last edge traversed:

τij ← (1 − φ)τij + φτ0,

where φ is the local evaporation rate and τ0 is the initial value of the pheromone. Updating also takes place at the end of the iteration and similarly to MMAS, the update is applied by the ant with the shortest tour. This is intended to reward edges belonging to shorter tours. The modified update is thus:

τij ← { (1 − ρ)τij + ρ?τij

if (i, j) belongs to the shortest tour; τij otherwise, ACS also differs from AS in the decision rule applied by the ants when travelling from cities i to j. The probability of transition depends on a uniformly distributed random variable q and a parameter q0. If q ≤ q0, then j = arg maxcil∈N {τilν β il}. If not, then the transition probability in Section 2.1 is used. This formulation suggests that an ant can decide, with probability q0, to exploit the experience accumulated by the ant colony based on the higher pheromone levels on the edges belonging to the shortest tours. Alternatively, with probability (1 − q0), an ant can apply a biased exploration that incorporates heuristic information as well as the edges belonging to shortest tours.

The RSA algorithms work on the encryption of data and information stored in the database. Encryption of data and information helps in maintaining the security of the data and information in the database (Peng, Wang & Xie 2017). The use of different encrypting technique helps in providing security to the data and information in the database.  The RSA algorithm has been one of the strongest algorithms for minimizing the security risks of data and information.

There are two major components of the RSA algorithm including:

Public-key encryption: In RSA algorithm, the encryption keys are by default public and decryption keys are private (Vatsalan et al. 2017). This can be done for maintaining the safety of the decryption key of the sender and the signature is properly verified during the decryption process.

Digital Signatures: The receiver must have to verify their identity by providing their digital signature before getting access to the data and information (Rogge et al. 2017).  The signature needs to be matched with the signature same as provided in the database of the verification system.

The RSA algorithm is discussed below:

The authors of RSA claim that “computing Me (mod n) requires at most 2 · log2 (e) multiplications and 2 · log2 (e) divisions” if we use their procedure below. It is important for us to know the number of steps it would take a computer to encrypt the message so we can see if a method is fast and efficient, or not. We now “exponentiate by repeated squaring and multiplication”:

Step 1. Let ekek−1…e1e0 be the binary representation of e.

Step 2. Set the variable C to 1.

Step 3. Repeat steps 3a and 3b for i = k, k − 1, ., 0:

Step 3a. Set C to the remainder of C 2 when divided by n.

Step 3b. If e_i = 1 then set C to the remainder of C · M when divided by n.

Step 4. Halt. Now C is the encrypted form of M.

Conclusion

It can be concluded that big data has been helping in storing a huge amount of data and information in the database. However, there have been different issues related to big data discussed in report. The six issues discussed in the paper has been properly analyzed in the report. Data Security in the cloud computing have been the major issues for big data analytics.  These issues have been creating problems for the organizations and users in maintaining their data and information over the internet. These issues have been penetrating in various organization.  The use of various tools have been discussed in report.  Big data has been storing data and information from traditional warehouses. This data has been properly maintained in the database of big data.  The use of big data analytics in modern world have helped in maintaining the data storage for the organization.    

There are some algorithms discussed in the report related to mitigation of data security issues in big data analytics.  The use of the Max-Min Ant algorithm has been discussed in a report for minimizing parallel data problems in big data.  The RSA algorithm has been helped in minimizing the security issues of the data by encrypting data and information in the database.  These algorithms have been helped in maintaining the security of data in the database.  Various organization have been paying for collecting the data and information from various sources in the market and storing them in big data database.

There are various recommendations provided in the report for the minimizing the security issues in big data.

Use of firewalls and antiviruses: Data transfer in the database has been a common procedure in the companies. Therefore, the most of the risks of cyber-attack has been in this part. Therefore, the transaction f data and information in the organization needs to be safe and secure.  This data might include some of the financial and personal information about the company.   Therefore, the breach of this type of data can make a huge loss to money and fame of the company in the market.   Therefore, use of firewalls and antivirus software is necessary to minimize this kind of issues. The use of firewalls has been restricting the entry of the harmful viruses and malware in the network of the database.  The use of the updated firewalls and antiviruses have helped in destroying malware and viruses entered into the network if database.

Encryption technology: The encryption technology have been helped in maintaining the security of data and information stored in a database. Data and information are encrypted in a packet before transferring from one party to another.  The use of encryption technique includes a key that is monitored by only sender and receiver.  The key is used for opening the data packets.  Therefore, data and information are secured from cyber-attacks and intruders during the transferring process.

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