Cited By
View all- Boulaaba AFaiz S(2018)Towards Big GeoData Mining and ProcessingInternational Journal of Organizational and Collective Intelligence10.4018/IJOCI.20180401048:2(60-73)Online publication date: 1-Apr-2018
Big Data Management: Advanced Issues and Approaches
Pages 44 - 55
Abstract
The objective of this article is to provide the advanced issues and approaches of big data management. The literature review indicates the overview of big data management; the aspects of Big Data Analytics BDA; the importance of big data management; the methods for big data management; the privacy and security concerns of big data management; and the big data management in the health care industry. Organizations that have been successful in working with effective big data management have accomplished this issue using data to help make sense of the information. The volume of data that companies are able to gather about customers and market conditions can provide business leaders with insights into new revenue and business opportunities, presuming they can spot the opportunities in vast amounts of data. The literature review analysis provides both practitioners and researchers an important understanding about big data management in modern organizations.
References
[1]
Abdelhafez, H. A. 2014. Big data problem, technologies and solutions. In Wang, J. Ed., Encyclopedia of business analytics and optimization pp. 338-350. Hershey, PA: IGI Global.
[2]
Al-Hamami, M. A. 2015. The impact of big data on security. In Al-Hamami, A., & Waleed al-Saadoon, G. Eds., Handbook of research on threat detection and countermeasures in network security pp. 276-298. Hershey, PA: IGI Global.
[3]
Amato, A., Venticinque, S., & di Martino, B. 2016. A distributed and scalable solution for applying semantic techniques to big data. In I. Management Association Ed., Big data: Concepts, methodologies, tools, and applications pp. 1091-1109. Hershey, PA: IGI Global.
[4]
Anderson, B., & Hardin, J. M. 2014. Harnessing the power of big data analytics. In Wang, J. Ed., Encyclopedia of business analytics and optimization pp. 1107-1116. Hershey, PA: IGI Global.
[5]
Ansari, S., Mohanlal, R., Poncela, J., Ansari, A., & Mohanlal, K. 2015. Importance of big data. In Zaman, N., Seliaman, M., Hassan, M., & Marquez, F. Eds., Handbook of research on trends and future directions in big data and web intelligence pp. 1-19. Hershey, PA: IGI Global.
[6]
Anshari, M., Alas, Y., Sabtu, N. I., & Yunus, N. 2016. A survey study of smartphones behavior in Brunei: A proposal of modelling big data strategies. International Journal of Cyber Behavior, Psychology and Learning, 61, 60-72.
[7]
Barbierato, E., Gribaudo, M., & Iacono, M. 2014. Performance evaluation of NoSQL big-data applications using multi-formalism models. Future Generation Computer Systems, 37, 345-353.
[8]
Batarseh, F. A., & Latif, E. A. 2016. Assessing the quality of service using big data analytics: With application to healthcare. Big Data Research, 4, 13-24.
[9]
Benjelloun, F., & Lahcen, A. A. 2015. Big data security: Challenges, recommendations and solutions. In Munir, K., Al-Mutairi, M., & Mohammed, L. Eds., Handbook of research on security considerations in cloud computing pp. 301-313. Hershey, PA: IGI Global.
[10]
Brajesh, S. 2016. Big data analytics in retail supply chain. In Kamath, N., & Saurav, S. Eds., Handbook of research on strategic supply chain management in the retail industry pp. 269-289. Hershey, PA: IGI Global.
[11]
Brunelle, J. F., & Boonthum-Denecke, C. 2012. Natural language processing tools. In Boonthum-Denecke, C., McCarthy, P., & Lamkin, T. Eds., Cross-disciplinary advances in applied natural language processing: Issues and approaches pp. 9-23. Hershey, PA: IGI Global.
[12]
Camacho, J. 2014. Visualizing big data with compressed score plots: Approach and research challenges. Chemometrics and Intelligent Laboratory Systems, 135, 110-125.
[13]
Campbell, J., Chang, V., & Hosseinian-Far, A. 2015. Philosophising data: A critical reflection on the "hidden" issues. International Journal of Organizational and Collective Intelligence, 51, 1-15.
[14]
Chang, R. M., Kauffman, R. J., & Kwon, Y. 2014. Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 67-80.
[15]
Chang, V. 2015. Towards a big data system disaster recovery in a private cloud. Ad Hoc Networks, 35, 65-82.
[16]
Chang, V. 2017. A cybernetics social cloud. Journal of Systems and Software, 124, 195-211.
[17]
Chang, V., Kuo, Y. H., & Ramachandran, M. 2016. Cloud computing adoption framework: A security framework for business clouds. Future Generation Computer Systems, 57, 24-41.
[18]
Chang, V., & Ramachandran, M. 2016. Towards achieving data security with the cloud computing adoption framework. IEEE Transactions on Services Computing, 91, 138-151.
[19]
Cheney, D. 2015. Big data, text mining, and news content: Where is the big data? In Gibbs, W., & McKendrick, J. Eds., Contemporary research methods and data analytics in the news industry pp. 133-151. Hershey, PA: IGI Global.
[20]
Côrte-Real, N., Oliveira, T., & Ruivo, P. 2017. Assessing business value of big data analytics in European firms. Journal of Business Research, 70, 379-390.
[21]
Elgendy, N., & Elragal, A. 2016. Big data analytics in support of the decision making process. Procedia Computer Science, 100, 1071-1084.
[22]
Esposito, C., Ficco, M., Palmieri, F., & Castiglione, A. 2015. A knowledge-based platform for big data analytics based on publish/subscribe services and stream processing. Knowledge-Based Systems, 79, 3-17.
[23]
Fan, S., Lau, R. Y. K., & Zhao, J. L. 2015. Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 21, 28-32.
[24]
Gebremeskel, G. B., Chai, Y., & He, Z. 2015. Demystifying big data in the cloud: Enhancing privacy and security using data mining techniques. In Bozkaya, B., & Singh, V. Eds., Geo-intelligence and visualization through big data trends pp. 264-304. Hershey, PA: IGI Global.
[25]
Gómez, C. Q., Villegas, M. A., García, F. P., & Pedregal, D. J. 2015. Big data and web intelligence for condition monitoring: A case study on wind turbines. In Zaman, N., Seliaman, M., Hassan, M., & Marquez, F. Eds., Handbook of research on trends and future directions in big data and web intelligence pp. 149-163. Hershey, PA: IGI Global.
[26]
Grottel, S., Heinrich, J., Weiskopf, D., & Gumhold, S. 2014. Visual analysis of trajectories in multi-dimensional state spaces. Computer Graphics Forum, 336, 310-321.
[27]
Gupta, M., & George, J. F. 2016. Toward the development of a big data analytics capability. Information & Management, 538, 1049-1064.
[28]
Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., & Chiroma, H. et al . 2016. The role of big data in smart city. International Journal of Information Management, 365, 748-758.
[29]
Hill, G., Datta, P., & Acar, W. 2015. Shifting perspectives: A process model for sense making under uncertainty. International Journal of Strategic Decision Sciences, 61, 33-52.
[30]
Hoque, M. R., & Bao, Y. 2016. Application of big data in healthcare: Opportunities, challenges and techniques. In I. Management Association Ed., Big data: Concepts, methodologies, tools, and applications pp. 1189-1208. Hershey, PA: IGI Global.
[31]
Hsu, C., Zeng, B., & Zhang, M. 2014. A novel group key transfer for big data security. Applied Mathematics and Computation, 249, 436-443.
[32]
Hu, W., & Kaabouch, N. 2014. Big data management, technologies, and applications pp. 1-342. Hershey, PA: IGI Global.
[33]
Jain, A., & Bhatnagar, V. 2016. Analysis of grievances in the banking sector through big data. International Journal of Service Science, Management, Engineering, and Technology, 74, 21-36.
[34]
Jain, A., Bhatnagar, V., & Sharma, P. 2017. Collaborative and clustering based strategy in big data. In Bhatnagar, V. Ed., Collaborative filtering using data mining and analysis pp. 140-158. Hershey, PA: IGI Global.
[35]
Jayanand, M., Kumar, M. A., Srinivasa, K. G., & Siddesh, G. M. 2016. Big data computing strategies. In I. Management Association Ed., Big data: Concepts, methodologies, tools, and applications pp. 793-813. Hershey, PA: IGI Global.
[36]
Jha, M., Jha, S., & O'Brien, L. 2017. Social media and big data: A conceptual foundation for organizations. In R. Chugh Ed., Harnessing social media as a knowledge management tool pp. 315-332. Hershey, PA: IGI Global.
[37]
Joseph, R. 2015. From data to vision: Big data in government. In Girard, J., Klein, D., & Berg, K. Eds., Strategic data-based wisdom in the big data era pp. 1-14. Hershey, PA: IGI Global.
[38]
Kaisler, S., Armour, F., Money, W., & Espinosa, J. A. 2015. Big data issues and challenges. In Khosrow-Pour, M. Ed., Encyclopedia of information science and technology 3rd ed., pp. 363-370. Hershey, PA: IGI Global.
[39]
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. 2014. Trends in big data analytics. Journal of Parallel and Distributed Computing, 747, 2561-2573.
[40]
Kasemsap, K. 2015a. The role of radio frequency identification in modern libraries. In Thanuskodi, S. Ed., Handbook of research on inventive digital tools for collection management and development in modern libraries pp. 361-385. Hershey, PA: IGI Global.
[41]
Kasemsap, K. 2015b. The role of cloud computing adoption in global business. In Chang, V., Walters, R., & Wills, G. Eds., Delivery and adoption of cloud computing services in contemporary organizations pp. 26-55. Hershey, PA: IGI Global.
[42]
Kasemsap, K. 2015c. Adopting cloud computing in global supply chain: A literature review. International Journal of Social and Organizational Dynamics in IT, 42, 49-62.
[43]
Kasemsap, K. 2015d. The role of business analytics in performance management. In Tavana, M., & Puranam, K. Eds., Handbook of research on organizational transformations through big data analytics pp. 126-145. Hershey, PA: IGI Global.
[44]
Kasemsap, K. 2015e. The role of business process reengineering in the modern business world. In Singh, A. Ed., Achieving enterprise agility through innovative software development pp. 87-114. Hershey, PA: IGI Global.
[45]
Kasemsap, K. 2016a. The fundamentals of business intelligence. International Journal of Organizational and Collective Intelligence, 62, 12-25.
[46]
Kasemsap, K. 2016b. Mastering big data in the digital age. In M. Singh & D. G. Eds., Effective big data management and opportunities for implementation pp. 104-129. Hershey, PA: IGI Global.
[47]
Kasemsap, K. 2016c. Multifaceted applications of data mining, business intelligence, and knowledge management. International Journal of Social and Organizational Dynamics in IT, 51, 57-69.
[48]
Kasemsap, K. 2017a. Software as a service, Semantic Web, and big data: Theories and applications. In Turuk, A., Sahoo, B., & Addya, S. Eds., Resource management and efficiency in cloud computing environments pp. 264-285. Hershey, PA: IGI Global.
[49]
Kasemsap, K. 2017b. Internet of Things and security perspectives: Current issues and trends. In Jeyanthi, N., & Thandeeswaran, R. Eds., Security breaches and threat prevention in the Internet of Things pp. 19-45. Hershey, PA: IGI Global.
[50]
Kasemsap, K. 2017c. Radio frequency identification and mobile ad-hoc network: Theories and applications. In Bhattacharyya, S., Das, N., Bhattacharjee, D., & Mukherjee, A. Eds., Handbook of research on recent developments in intelligent communication application pp. 63-95. Hershey, PA: IGI Global.
[51]
Kasemsap, K. 2017d. Mastering electronic health record in global health care. In Wickramasinghe, N. Ed., Handbook of research on healthcare administration and management pp. 222-242. Hershey, PA: IGI Global.
[52]
Kasemsap, K. 2017e. Mastering business process management and business intelligence in global business. In Tavana, M., Szabat, K., & Puranam, K. Eds., Organizational productivity and performance measurements using predictive modeling and analytics pp. 192-212. Hershey, PA: IGI Global.
[53]
Kemp, R. 2014. Legal aspects of managing big data. Computer Law & Security Report, 305, 482-491.
[54]
Kim, W., Jeong, O., & Kim, C. 2016. A holistic view of big data. In I. Management Association Ed., Big data: Concepts, methodologies, tools, and applications pp. 73-84. Hershey, PA: IGI Global.
[55]
Klepac, G., & Berg, K. L. 2015. Proposal of analytical model for business problems solving in big data environment. In Girard, J., Klein, D., & Berg, K. Eds., Strategic data-based wisdom in the big data era pp. 209-228. Hershey, PA: IGI Global.
[56]
Kolodziej, J., & Xhafa, F. 2012. Integration of task abortion and security requirements in GA-based meta-heuristics for independent batch grid scheduling. Computers & Mathematics with Applications Oxford, England, 632, 350-364.
[57]
Kolomvatsos, K., Anagnostopoulos, C., & Hadjiefthymiades, S. 2015. An efficient time optimized scheme for progressive analytics in big data. Big Data Research, 24, 155-165.
[58]
Kosala, R., & Kumaradjaja, R. 2015. Big data analytics for business. In Khosrow-Pour, M. Ed., Encyclopedia of information science and technology 3rd ed., pp. 355-362. Hershey, PA: IGI Global.
[59]
Koumpouros, Y. 2015. Big data in healthcare. In Healthcare administration: Concepts, methodologies, tools, and applications pp. 23-46. Hershey, PA: IGI Global.
[60]
Koyluoglu, O., Koksal, C., & El Mamal, H. 2012. On secrecy capacity scaling in wireless networks. IEEE Transactions on Information Theory, 585, 3000-3015.
[61]
Kshetri, N. 2014. Big datas impact on privacy, security and consumer welfare. Telecommunications Policy, 3811, 1134-1145.
[62]
Kwon, O., Lee, N., & Shin, B. 2014. Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 343, 387-394.
[63]
Larson, D., & Chang, V. 2016. A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 365, 700-710.
[64]
Loebbecke, C., & Picot, A. 2015. Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 243, 149-157.
[65]
Mishra, B. K., & Sahoo, A. K. 2016. Application of big data in economic policy. In Mallick, P. Ed., Research advances in the integration of big data and smart computing pp. 178-197. Hershey, PA: IGI Global.
[66]
Moura, J., Batista, F., Cardoso, E., & Nunes, L. 2015. Intelligent management and efficient operation of big data. In Zaman, N., Seliaman, M., Hassan, M., & Marquez, F. Eds., Handbook of research on trends and future directions in big data and web intelligence pp. 105-129. Hershey, PA: IGI Global.
[67]
Moura, J., & Serrão, C. 2015. Security and privacy issues of big data. In Zaman, N., Seliaman, M., Hassan, M., & Marquez, F. Eds., Handbook of research on trends and future directions in big data and web intelligence pp. 20-52. Hershey, PA: IGI Global.
[68]
Nirmala, M. B., & Raj, P. 2014. Big data computing and the reference architecture. In Raj, P., & Deka, G. Eds., Handbook of research on cloud infrastructures for big data analytics pp. 22-37. Hershey, PA: IGI Global.
[69]
Orike, S., & Brown, D. 2016. Big data management: An investigation into wireless and cloud computing. International Journal of Interdisciplinary Telecommunications and Networking, 84, 34-50.
[70]
Pokorny, J., & Stantic, B. 2016. Challenges and opportunities in big data processing. In Ma, Z. Ed., Managing big data in cloud computing environments pp. 1-24. Hershey, PA: IGI Global.
[71]
Raman, A. C. 2014. Storage infrastructure for big data and cloud. In Raj, P., & Deka, G. Eds., Handbook of research on cloud infrastructures for big data analytics pp. 110-128. Hershey, PA: IGI Global.
[72]
Ranjan, J. 2014. Big data applications in healthcare. In Smith, P., & Cockburn, T. Eds., Impact of emerging digital technologies on leadership in global business pp. 202-214. Hershey, PA: IGI Global.
[73]
Ranjan, R., Mitra, K., & Georgakopoulos, D. 2013. MediaWise cloud content orchestrator. Journal of Internet Services and Applications, 41, 1-14.
[74]
Rekha, J. H., & Parvathi, R. 2015. Survey on software project risks and big data analytics. Procedia Computer Science, 50, 295-300.
[75]
Shen, Y., & Zhang, Y. 2014. Transmission protocol for secure big data in two-hop wireless networks with cooperative jamming. Information Sciences, 281, 201-210.
[76]
Sucharitha, V., Subash, S. R., & Prakash, P. 2014. Visualization of big data: Its tools and challenges. International Journal of Applied Engineering Research, 918, 5277-5290.
[77]
Ta, A. D., Tanque, M., & Washington, M. 2015. A specification framework for big data initiatives. In Girard, J., Klein, D., & Berg, K. Eds., Strategic data-based wisdom in the big data era pp. 257-274. Hershey, PA: IGI Global.
[78]
Thilagavathi, M., Lopez, D., & Murugan, B. S. 2014. Middleware for preserving privacy in big data. In Raj, P., & Deka, G. Eds., Handbook of research on cloud infrastructures for big data analytics pp. 419-443. Hershey, PA: IGI Global.
[79]
Wahi, A. K., Medury, Y., & Misra, R. K. 2015. Big data: Enabler or challenge for enterprise 2.0. International Journal of Service Science, Management, Engineering, and Technology, 62, 1-17.
[80]
Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. 2016. Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.
[81]
Wang, H., Liu, W., & Soyata, T. 2014. Accessing big data in the cloud using mobile devices. In Raj, P., & Deka, G. Eds., Handbook of research on cloud infrastructures for big data analytics pp. 444-470. Hershey, PA: IGI Global.
[82]
Wang, L., Chen, D., Zhao, J., & Tao, J. 2012. Resource management of distributed virtual machines. International Journal of Ad Hoc and Ubiquitous Computing, 102, 96-111.
[83]
Wang, Y., & Wiebe, V. J. 2014. Big data analytics on the characteristic equilibrium of collective opinions in social networks. International Journal of Cognitive Informatics and Natural Intelligence, 83, 29-44.
[84]
Wassan, J. T. 2016. Big data paradigm for healthcare sector. In I. Management Association Ed., Big data: Concepts, methodologies, tools, and applications pp. 570-587. Hershey, PA: IGI Global.
[85]
Xuan, P., Ligon, W. B., Srimani, P. K., Ge, R., & Luo, F. 2017. Accelerating big data analytics on HPC clusters using two-level storage. Parallel Computing, 61, 18-34.
[86]
Yang, C., Zhang, X., Zhong, C., Liu, C., Pei, J., Ramamohanarao, K., & Chen, J. 2014. A spatiotemporal compression based approach for efficient big data processing on cloud. Journal of Computer and System Sciences, 808, 1563-1583.
[87]
Zhang, W., Wang, L., Song, W., Ma, Y., Liu, D., Liu, P., & Chen, D. 2013. Towards building a multi-datacenter infrastructure for massive remote sensing image processing. Concurrency and Computation, 2512, 1798-1812.
[88]
Zhang, X., Liu, C., Nepal, S., Yang, C., Dou, W., & Chen, J. 2014. A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud. Journal of Computer and System Sciences, 805, 1008-1020.
[89]
Zhao, J., Wang, L., Tao, J., Chen, J., Sun, W., Ranjan, R., & Georgakopoulos, D. et al . 2014. A security framework in G-Hadoop for big data computing across distributed cloud data centres. Journal of Computer and System Sciences, 805, 994-1007.
[90]
Zou, H., Yu, Y., Tang, W., & Chen, H. W. M. 2014. FlexAnalytics: A flexible data analytics framework for big data applications with I/O performance improvement. Big Data Research, 1, 4-13.
Recommendations
Big data and ICT applications: A study
ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive StrategiesBig Data is used to manage the data due to their large size and complexity, because it can't be handled with the traditional methods and the current technology or tools used for that. Big Data mining is populated with 5 V's volume, variability, velocity,...
Challenges for MapReduce in Big Data
SERVICES '14: Proceedings of the 2014 IEEE World Congress on ServicesIn the Big Data community, MapReduce has been seen as one of the key enabling approaches for meeting continuously increasing demands on computing resources imposed by massive data sets. The reason for this is the high scalability of the MapReduce ...
Energy-efficient hadoop for big data analytics and computing: A systematic review and research insights
AbstractAs the demands for big data analytics keep growing rapidly in scientific applications and online services, MapReduce and its open-source implementation Hadoop gained popularity in both academia and enterprises. Hadoop provides a highly feasible ...
Highlights- This paper presents the new viewpoints/insights in improving the energy efficiency of Hadoop.
- Present valuable and feasible solutions towards improving the energy efficiency of Hadoop.
- Propose five categories of optimizing the ...
Comments
Information & Contributors
Information
Published In
Publisher
IGI Global
United States
Publication History
Published: 01 July 2017
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 08 Feb 2025
Other Metrics
Citations
Cited By
View all- Boulaaba AFaiz S(2018)Towards Big GeoData Mining and ProcessingInternational Journal of Organizational and Collective Intelligence10.4018/IJOCI.20180401048:2(60-73)Online publication date: 1-Apr-2018