Data Mining and Knowledge Engineering

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

A Survey on Big Data-Concepts, Analytics and its Tools


Affiliations
1 Department of Information Technology, St Joseph’s College - Tiruchirappalli, India
     

   Subscribe/Renew Journal


Big data is related with another age of innovations and structures, which can outfit the estimation of greatly substantial volumes of extremely fluctuated information through ongoing preparing and investigation. It includes changes in information composes, aggregation speed, and information volume, Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper’s primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. In this paper, focus on concepts, methods and analytics used in big data.


Keywords

Big Data, Hadoop, Map Reduce, Security.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.

  • Tekiner F. and Keane J.A., Systems, Man and Cybernetics (SMC), “Big Data Framework” 2013 IEEE International Conference on 13–16 Oct. 2013, 1494–1499.

  • Nguyen T.D., Gondree M.A., Khosalim, J.; Irvine, “Towards a Cross Domain MapReduce Framework “IEEE C.E. Military Communications Conference, MILCOM 2013, 1436 – 1441.

  • Kyuseok Shim, MapReduce Algorithms for Big Data Analysis, DNIS 2013, LNCS 7813, pp. 44–48, 2013

  • M. Aharon, M. Elad, and A. Bruckstein, “K-SVD:An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Processing, vol.54, no. 11, pp. 4311–4322, Nov. 2006.

  • M. Zaharia et al. “Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling”. In EuroSys,2010.

  • M. Zaharia, D. Borthakur, J. S. Sarma, K. Elmeleegy, S. Shenker, and I. Stoica, “Job scheduling for multi-user mapreduce clusters,” EECS Department, University of California, Berkeley, Tech. Rep.,Apr 2009.

  • J. Gantz, D. Reinsel, "Extracting value from chaos", IDC iView, 2011, pp 1–12.

  • Canada Inforoute, "Big Data Analytics in health", White Paper, Full Report, April 2013.

  • Gartner, "IT glossary: big data" [webpage on the Internet]. Stamford, CT; 2012. Retrieved from: http://www.gartner.com/it-glossary/big-data.

  • E. Mcnulty, "Understanding Big Data: The Seven V’s", Dataconomy, May 22, 2014, Retrieved from: http://dataconomy.com/seven-vs-bigdata/.

  • A. Alexandru, D. Coardos, "BD in Tackling Energy Efficiency in Smart City", Scientific Bulletin of the Electrical Engineering Faculty, vol. 28, no. 4, pp. 14-20, 2014, Bibliotheca Publishing House, ISSN 1843-6188.

  • Frost & Sullivan White Paper, "Drowning in Big Data? Reducing Information Technology Complexities and Costs For Healthcare Organizations", 2012,

  • Watson, H.J. (2013a) “All about Analytics", International Journal of Business Intelligence Research, 2, pp.13-28.

  • Power, D.J. (2007) “A Brief History of Decision Support Systems", DSSResources.com, http://DSSResources.COM/history/dsshistory.html, version 4.0 (current March 7, 2014).

  • Watson, H. J. (2009a) “Tutorial: Business Intelligence – Past, Present, and Future", Communications of the Association for Information Systems, (25)39 (current March 7, 2014).


Abstract Views: 248

PDF Views: 4




  • A Survey on Big Data-Concepts, Analytics and its Tools

Abstract Views: 248  |  PDF Views: 4

Authors

A. Antony Prakash
Department of Information Technology, St Joseph’s College - Tiruchirappalli, India
H. Aarthi
Department of Information Technology, St Joseph’s College - Tiruchirappalli, India

Abstract


Big data is related with another age of innovations and structures, which can outfit the estimation of greatly substantial volumes of extremely fluctuated information through ongoing preparing and investigation. It includes changes in information composes, aggregation speed, and information volume, Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper’s primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. In this paper, focus on concepts, methods and analytics used in big data.


Keywords


Big Data, Hadoop, Map Reduce, Security.

References