Abstract

Big Data is a new term used to identify the datasets that due to their large size and complexity, we cannot manage them with our current methodologies or data mining software tools. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variability, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. We present in this issue, a broad overview of the topic, its current status, controversy, and forecast to the future. We introduce four articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining

References

• IBM, Data growth and standards. Retreived from: http://www. ibm. com/developerworks/xml/library/x-datagrowth/index. html?ca=drs- [Accessed 13th March 2014].
• G. Press, G, 'A Very Short History Of Big Data, An Article of Forbes', Retreived from http://www. forbes. com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/[ Accessed 13th March 2014]
• M. D. Devignes, M. Smail, E. Bresso, A. Coulet, C. Raïssi, A. Napoli, , "Knowledge discovery from biological Big Data : scalability issues", International Journal of Metadata, Semantics and Ontologies, vol. 5, Iss. 3, pp. 184-193, 2010
• B. Schmarzo, B, Big Data: Understanding How Data Powers Big Business, John Wiley & Sons, Business & Economics, pp. 240 pages, 2013
• K. Davis, K, Ethics of Big Data: Balancing Risk and Innovation, O-Reilly Media, Computers, pp. 82 pages, 2012
• N. Veeranjaneyulu, M. N. , Bhat, A. Raghunath, "Approaches for Managing and Analyzing Unstructured Data", International Journal on Computer Science and Engineering, Vol. 6 No. 01, 2014
• C. F. McCaul, B. W. Scotney, G. P. Parr, S. I. McClean, "A Cloud based End-To-End Big Data System", PGNet, ISBN: 978-1-902560-27-4, 2013
• M. Peters, J. Buffington, and M. Keane, 'Cloud Storage: the next Frontier for tape', White paper of Enterprise Strategy Group, 2013
• M. Minelli, M. Chambers, A. Dhiraj, Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses, John Wiley & Sons, Business & Economics, pp. 224 pages, 2012
• J. K. Pal, "Usefulness and applications of data mining in extracting information from different perspective", Annals of Library and Information Studies, Vol. 58, pp. 7-16, 2011
• D. Boyd and K. Crawford. Critical Questions for Big Data. Information, Communication and Society, 15(5):662{679, 2012
• J. Lin. MapReduce is Good Enough? If All You Have is a Hammer, Throw Away Everything That's Not a Nail! CoRR, abs/1209. 2191, 2012
• N. Taleb. Antifragile: How to Live in a World We Don't Understand. Penguin Books, Limited, 2012
• A. Petland. Reinventing society in the wake of big data. Edge. org, http://www. edge. org/conversation/reinventing-society-in-the-wake-of-big-data, 2012
• D. Laney. 3-D Data Management: Controlling Data Volume, Velocity and Variety. META Group Research Note, February 6, 2001
• U. Fayyad. Big Data Analytics: Applications and Opportunities in On-line Predictive Modeling. http://big-data-mining. org/keynotes/#fayyad, 2012
• Gartner, http://www. gartner. com/it-glossary/bigdata
• UN Global Pulse, http://www. unglobalpulse. org
• Apache Hadoop, http://hadoop. apache. org
• P. Zikopoulos, C. Eaton, D. deRoos, T. Deutsch, and G. Lapis. IBM Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Companies,Incorporated, 2011. SIGKDD
• L. Neumeyer, B. Robbins, A. Nair, and A. Kesari. S4: Distributed Stream Computing Platform. In ICDM Workshops, pages 170{177, 2010.
• Storm, http://storm-project. net.
• Apache Mahout, http://mahout. apache. org.
• R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2012. ISBN 3-900051-07-0
• A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer. MOA: Massive Online Analysis http://moa. cms. waikato. ac. nz/. Journal of Machine Learning Research (JMLR), 2010
• SAMOA, http://samoa-project. net, 2013
• J. Langford. Vowpal Wabbit, http://hunch. net/~vw/,2011
• U. Kang, D. H. Chau, and C. Faloutsos. PEGASUS:Mining Billion-Scale Graphs in the Cloud. 2012
• Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein. Graphlab: A new parallel framework for machine learning. In Conference on Uncertainty in Arti_cial Intelligence (UAI), Catalina Island, California, July 2010
• J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A. Hung Byers, Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute, 2011
• http://www. ivrsdevelopment. com/ivrs_education. htm
• http://persistenceplusnetwork. com/
• https://www. pa. nesinc. com/TestView. aspx?f=HTML_FRAG/PA001_TestPage. html
• G. Fox, Big Data in the Cloud: Research and Education, PPAM, 2013
• http://www. google. com/enterprise/apps/education/products. html
• http://www. microsoft. com/education/en-cy/solutions/Pages/live-edu. aspx
• http://aws. amazon. com/education/
• C. Romero, S. Ventura, Educational Data Mining: A Review of the State-of-the-Art, Transactions on Systems, Man, and Cybernetics, IEEE Transactions On Systems, Man, And Cybernetics, 2010
• R. Sallam, M. Beyer, N. Heudecker, Key trends in Big Data technologies, An article from The Connected Business, 2013
• R. Schutt, Big Data Domain Surfing, An Article from Introduction to data Science, Columbia University, 2012. Retreived from http://columbiadatascience. com/2012/09/11/big-data-domain-surfing-part-1/
• O. Hasan, B. Habegger, L. Brunie, N. Bennani, E. Damiani, A Discussion of Privacy Challenges in User Profiling with Big Data Techniques: The EEXCESS Use Case, IEEE International Congress on Big Data, 2013
• C. Parker. Unexpected challenges in large scale machine learning. In Proceedings of the 1st International Work-shop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, BigMine '12, pages 1{6, New York, NY, USA, 2012. ACM
• V. Gopalkrishnan, D. Steier, H. Lewis, and J. Guszcza. Big data, big business: bridging the gap. In Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, Big-Mine '12, pages 7{11, New York, NY, USA, 2012. ACM
• N. Marz and J. Warren. Big Data: Principles and best practices of scalable realtime data systems. Manning Publications, 2013
• D. Feldman, M. Schmidt, and C. Sohler. Turning big data into tiny data: Constant-size coresets for k-means, pca and projective clustering. In SODA, 2013
• B. Efron. Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Institute of Mathematical Statistics Monographs. Cambridge University Press, 2010
• Y. Dandawate, "Big Data: Challenge and Opportunities", Business Innovations through technology, vol. 11, No. 1, 2013
• W. Fan, & A. Bifet, "Mining Big Data: Current Status, and Forecast to the Future", ACM-SIGKDD Explorations, Vol. 14, Iss. 2, pp. 1-5, 2012