Commenced in January 2007
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Mining Big Data in Telecommunications Industry: Challenges, Techniques, and Revenue Opportunity

Authors: Hoda A. Abdel Hafez


Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.

Keywords: Mining Big Data, Big Data, Machine learning, Data Streams, Telecommunication.

Digital Object Identifier (DOI):

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[1] D. Che, M. Safran and Z. Peng, “From Big Data to Big Data Mining: Challenges, Issues, and Opportunities”, DASFAA, workshops, LNCN 7827, Springer, 2013, PP 1-15.
[2] Cisco Analysis, “Cisco global cloud index: forecast and methodology 2013-2018” White Paper, 2014.
[3] Fan W. and Bifet A. (2013), “Mining big data: Current status and forecast to the future” SIGKDD Explarations Vol. 14(2), PP. 1-5.
[4] A. Tole, “Big data challenges” Database Systems Journal, Vol. IV (3), 2013, PP. 31-40.
[5] P. Sharma and C. Navdeti “Securing big data Hadoop: A review of security issues, threats and solution”, Vol. 5(2), 2014, PP. 2126-2131.
[6] T. Mitha and V. Kumar, “Application of big data in data mining”, International Journal of Emerging Technology and advanced Engineering, Vol. 3(7), 2013, PP. 390-393.
[7] M. TRIFU and M. IVAN, “Big Data: present and future”, Database Systems Journal, vol. 5(1), 2014, PP. 32-41.
[8] X. Wu and X. Zhu, “Data Mining with Big Data”, IEEE transactions on Knowledge and Data Engineering, Vol. 26 (1), 2014, PP. 97-107.
[9] O. Tene, J. Polonetsky, “Privacy in the Age of big data: A Time for Big Decisions”, Stanford Law Review Online, vol. 64, 2012, pp. 63-69.
[10] A. Franco-Arcega, J. Carrasco-Ochoa, G. Sánchez-Díaz, and J. Martínez-Trinidad, “Decision Tree based Classifiers for Large Datasets”, Computacióny Sistemas Vol. 17(1), 2013, pp. 95-102.
[11] C. Yada, S. Wang and M. Kumar, “Algorithm and approaches to handle large data survey”, IJCSN International Journal of Computer Science and Network, Vol 2(3), ISSN (Online): 2277-5420, 2013.
[12] Y. Lu and C. Fahn, “Hierarchical Artificial Neural Networks for recognizing high similar large data sets”, Proceeding of the sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 2007, PP. 1930-1935.
[13] M. Vijayalakshmi and M. Renuka devi, “A Survey of Different Issue of Different Clustering Algorithms Used in Large Data sets”, IJCSN International Journal of Computer Science and software Engineering, Vol 2(3), 2012, PP. 305-307.
[14] H. Yu, J. Yang, and J. Han, “Classifying Large Data Sets Using SVMs with Hierarchical Clusters”, SIGKDD’03 Washington, DC, USA, 2003.
[15] R. Mahajan, A. Thangavelu and M. Shahakar, “Data Mining Techniques for Identifying Temporal Patterns of Time Series Data”, Journal of Engineering Research and Applications (IJERA) Vol. 2(6), 2012, pp.185-187 185.
[16] R. Ding, Q. Wang, Y. Dang, Q. Fu, H. Zhang, D. Zhang, “YADING: Fast Clustering of Large-Scale Time Series Data”, Proceedings of the VLDB Endowment, Vol. 8(5), 2015, PP. 473-484.
[17] D. Parikh and P. Tirkha, “Data mining and data stream mining – open source tools”, International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), Vol. 2(10), 2013, PP. 5234-5239.
[18] T. Trambadiya and P. Bhanodia, “A comparative study of stream data mining and innovative technology”, International Journal of Engineering and innovative technology (IJEIT), Vol. 2(3), 2012, PP. 149-154.
[19] A. Bifet and R. Gavalda “Adaptive parameter-free learning from evolving data streams”, Proceeding of 8th International Symposium on Intelligent Data Analysis, IDA in the series lecture notes in computer science, Vol. 5772, 2009, PP. 249-260.
[20] G. Hulten, L. Spencer and P. Domingos, “Mining time-changing data streams”, proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, 2001, PP 97-106.
[21] A. Jovic, K. Brkic and N. Bogunovic, “An overview of free software tools for general data mining”, 37th International Conventiion on Information & Communication Technology Electronics & Microelectronics., 2014, PP. 1112-1117.
[22] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python”, JMLR, Vol. 12, 2011, PP.2825-2830.
[23] A. Bifet, “Mining big data in real time”, infomatica, Vol. 37, 2013, PP. 15-20.
[24] M. Berthold, N. Cebron, F. Dill, T. Gabriel, T. Kotter, T. Meinl, P. Ohl, K. Thiel, and B. Wiswedel, “KNIME – The Konstanz Information Miner: Version 2.0 and Beyond”, ACM SIGKDD Explorations, Vol. 11(1), 2009, PP. 26-31.
[25] A. Bifet, G. Holmes, R. Kirkby. and B. Pfahringer, “MOA: Massive online analysis”, Journal of Machine Learning Researches, Vol. 11, 2010, PP. 1601-1604.
[26] RapidMiner Review, 2015,
[27] G. Morales and A. Bifet, “SAMOA: Scalable Advanced Massive Online Analysis”, Journal of Machine Learning Research, Vol. 16, 2015, PP.149-153.
[28] G. Weiss, “Data mining in Telecommunication”, in O. Maimon and L. Rokach (Eds) Data mining & Knowledge discovery handbook: A complete guide for practitioners and research, Kluwer Academic publisher, 2005.
[29] M. Joseph, “Data mining and business intelligence applications in telecommunication Industry” International Journal of Engineering and advanced Technology (IJEAT), 2013.
[30] R. Dam, “Big data a sure thing for telecommunications: telecom’s future in big data”, International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2013
[31] G. Olle and S. Cai, “A hybrid churn prediction model in Mobile telecommunication industry”, International Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 4(1), 2014, PP. 55-62.
[32] V. Yeshwanth, V. Raj and M. Saravanan, “Evolutionary Churn Prediction in Mobile Networks Using Hybrid Learning” Proceeding of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference, 2011.
[33] Y. Liu, and Y. Zhuang, “Research Model of Churn Prediction Based on Customer Segmentation and Misclassification Cost in the Context of Big Data”, Journal of Computer and Communications, Vol. 3, 2015, PP. 87-93
[34] C. Hilas, P. Mastorocostas and I. Rekanos, “Clustering of Telecommunications User Profiles for Fraud Detection and Security Enhancement in Large Corporate Networks: A case Study”, An International Journal Applied Mathematics & Information Sciences, Vol. 9(4), PP. 2015, 1709-1718.
[35] C. Cortes and D. Pregubon, “Signature-Based Methods for Data Streams”, Data Mining and Knowledge Discovery, Kluwer Academic Publishers. Vol. 5, 2001, PP. 167–182.
[36] M. Musolesi “Big Mobile Data Mining: Good or Evil?” IEEE Internet Computing, 2014, PP. 2-5.
[37] S. Nakajima and J. Gaudemer, “Big data for Telcos: How big data can get new revenue and reduce costs”, IDATE Research, December 2013.