Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

BigData Analytical Challenges with IOT


Affiliations
1 Department of Computer Science and Engineering, JNTUAC, India
2 Department of Computer Science and Engineering, VLITS, Vadlamudi, India
3 Department of Computer Science and Engineering, JNTUP, India
     

   Subscribe/Renew Journal


Data in today's world is much more complex than ever before. With the technological advancements businesses are able to easily gather data both at the organizational level as well as from the external data sources. The accumulated data is huge and diverse with structured, unstructured components or data generated by Internet-of-Things (IOT). Businesses are in dire need to analyze these sets of data to derive a better value to the organizations. With analytics becoming central to all the business strategies, this paper presents a review of the challenges which the organizations have to take into account while dealing with these complex data residing in the data stores. Apart from the volumes and complexity of data, IOT brings in new challenges in the form of security to the BigData systems as a whole and data in particular. This paper also reviews the conceptual studies which have attributed to the growth of Bigdata technologies to provide business analytics by ensuring security to the data.

Keywords

BigData, BigData Analytics, Challenges, Internet-of-Things, Security.
Subscription Login to verify subscription
User
Notifications
Font Size


  • J. Gantz, and D. Reinsel, “The Digital Universe in 2020: Big data, bigger digital shadows, and biggest growth in the Far East,” IDC-EMC Corporation, 2012. Available: http://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf
  • C. Dobre, and F. Xhafa, F. “Intelligent services for big data science,” Future Generation Computer Systems, vol. 37, pp. 267-281, 2014.
  • A. McAfee, and E. Brynjolfsson, (2012). “Big data: The management revolution,” Harvard Business Review.
  • J. Chen, Y. Chen, X. Du, C. Li, J. Lu, S. Zhao, and X. Zhou, (2013). “Big data challenge: A data management perspective,” Frontiers of Computer Science, vol. 7, no. 2, pp. 157-164, 2013.
  • G. Press, Internet of Things (IoT) “Predictions from Forrester, Machina Research, WEF, Gartner, IDC,” 2016. Available: http://www.forbes.com/sites/gilpress/2016/01/27/internet-of-things-iot-predictions-from-forrester-machina-research-wef-gartner-idc/#4b1601546be6.
  • R. Akerkar, “Big Data Computing,” Florida, USA: CRC Press, Taylor & Francis Group, 2014.
  • R. V. Zicari, R. V. (2014). “Big Data: Challenges and Opportunities,” In R. (Ed.), Big data computing. Florida, USA: CRC Press, Taylor & Francis Group, pp. 103-128, 2014.
  • T. Shah, F. Rabhi, and P. Ray, “Investigating an ontology-based approach for Big Data analysis of inter-dependent medical and oral health conditions,” Cluster Computing, vol. 18, no. 1, pp. 351-367, 2015.
  • Z Liao, Q. Yin, Y. Huang, and L. Sheng, “Management and application of mobile big data,” International Journal of Embedded Systems, vol. 7, no. 1, pp. 63-70, 2014.
  • A. Gandomi, and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” International Journal of Information Management, vol. 35, no. 2, pp. 137-144, 2015.
  • U. Sivarajah, M. M. Kamal, Z. Irani, and V. Weerakkody, “Critical analysis of Big Data challenges and analytical methods,” Journal of Business Research, vol. 70, pp. 263-286, 2017.
  • J. Bradley, J. Barbier, and D. Handler, “Embracing the Internet of Everything to Capture Your Share of $14.4 Trillion,” Available: http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoE_Economy.pdf
  • H. Chen, R. H. Chiang, and V. C. Storey, V. C. “Business intelligence and analytics: From Big Data to big impact,” MIS Quarterly, Vol. 36, no. 4, pp. 1165-1188, 2012.
  • X. Zhang, Y. Hu, K. Xie, W. Zhang, L. Su, and M. Liu, “An evolutionary trend reversion model for stock trading rule discovery,” Knowledge-based Systems, vol. 79, pp. 27-35, 2015.
  • C. P. Chen, and C. Y. Zhang, “Data Intensive applications, challenges, techniques and technologies: A survey on Big Data,” Information Sciences, 275, pp. 314-347, 2014.
  • E. Y. E. Gorodov, and V. V. E. Gubarev, “Analytical review of data visualization methods in application to Big Data,” Journal of Electrical and Computer Engineering, pp. 22, 2013.
  • J. Paris, J. S. Donnal, and S.B. Leeb, D. B. Nilm, “The non-intrusive load monitor data-base,” IEEE Transactions on Smart Grid, vol. 5, no. 5, pp. 2459-2467, 2014.
  • B. B. Ahamed, T. Ramkumar, and S. Hariharan, “Data integration progression in large data source using mapping affinity,” In 7th International Conference on Advanced Software Engineering and Its Applications (ASEA), 2014.
  • J. Liu, and X. Zhang, “Data integration in fuzzy XML documents,” Information Sciences, vol. 280, pp. 82-97, 2014.
  • D. Agarwal, P. Bernstein., E. Bertino, S. Davidson, U. Dayal, and M. Franklin, “Challenges and opportunities with big data:A community white paper developed by leading researchers across the United States,” Whitepaper, Computing Community Consortium, 2012.
  • A. Gani, “A survey on indexing techniques for big data: taxonomy and performance evaluation,” Knowledge and Information Systems, vol. 46, no. 2, pp. 241-284, 2016.
  • M. Chen, V. C. Leung, and S. Mao, “Directional controlled fusion in wireless sensor networks,” Mobile Networks and Applications, vol. 14, no. 2, pp. 220-229, 2009.
  • Y. Sun, “Mining knowledge from interconnected data: A heterogeneous information network analysis approach,” Proceedings of the VLDB Endowment, vol. 5, no. 12, pp. 2022-2023, 2012.
  • M. Chen, “Itinerary planning for energy-efficient agent communications in wireless sensor networks,” IEEE Transactions on Vehicular Technology, vol. 60, no. 7, pp. 3290-3299, 2011.
  • D. Zhang, “A taxonomy of agent technologies for ubiquitous computing environments,” TIIS, vol. 6, no. 2, pp. 547-565, 2012.

Abstract Views: 364

PDF Views: 2




  • BigData Analytical Challenges with IOT

Abstract Views: 364  |  PDF Views: 2

Authors

Lalitha Balla
Department of Computer Science and Engineering, JNTUAC, India
Chavva Ravi Kishore Reddy
Department of Computer Science and Engineering, VLITS, Vadlamudi, India
A. V. L. N. Sujith
Department of Computer Science and Engineering, JNTUP, India

Abstract


Data in today's world is much more complex than ever before. With the technological advancements businesses are able to easily gather data both at the organizational level as well as from the external data sources. The accumulated data is huge and diverse with structured, unstructured components or data generated by Internet-of-Things (IOT). Businesses are in dire need to analyze these sets of data to derive a better value to the organizations. With analytics becoming central to all the business strategies, this paper presents a review of the challenges which the organizations have to take into account while dealing with these complex data residing in the data stores. Apart from the volumes and complexity of data, IOT brings in new challenges in the form of security to the BigData systems as a whole and data in particular. This paper also reviews the conceptual studies which have attributed to the growth of Bigdata technologies to provide business analytics by ensuring security to the data.

Keywords


BigData, BigData Analytics, Challenges, Internet-of-Things, Security.

References