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J.S. Shyam Mohan, P. Shanmugapriya and Bhamidipati Vinay Pawan Kumar

, Xingquan Zhu, Gong-Qing Wu, and Wei Ding, Data Mining with Big Data , Ieee Transactions On Knowledge And Data Engineering, 2014, Vol. 26, No. 1, pp. 97-107 5. C.T. Chu, S.K. Kim, Y.A. Lin, Y. Yu, G.R. Bradski, A.Y. Ng, and K. Olukotun, 2006, Map-Reduce for Machine Learning on Multicore, Proc. 20th Ann. Conf. Neural Information Processing Systems (NIPS ’06), pp. 281-288 6. Ron Bekkerman, Shlomo Zilberstein, James Allan-Web Page Clustering using Heuristic Search in the Web Graph, IJCAI'07, Proceedings of the 20th international joint conference on Artifical

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Jerzy Stefanowski, Krzysztof Krawiec and Robert Wrembel

References Ahmadov, A., Thiele, M., Eberius, J., Lehner, W. and Wrembel, R. (2015). Towards a hybrid imputation approach using web tables, IEEE/ACM International Symposium on Big Data Computing (BDC), Limassol, Cyprus, pp. 21-30. Bekkerman, R., Bilenko, M. and Langford, J. (2011). Scaling Up Machine Learning: Parallel and Distributed Approaches, Cambridge University Press, New York, NY. Benjelloun, O., Garcia-Molina, H., Menestrina, D., Su, Q., Whang, S.E. and Widom, J. (2009). Swoosh: A generic approach to

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Shilpa Balan and Joseph Otto

References [1] R Project. The R Project for Statistical Computing. [Online]. Available: https://www.r-project.org/ Accessed: Feb. 15, 2016. [2] R. Gershon, M. Pogorzelska, K. Qureshi, P. Stone, S. Samar, L. Westra, M. Damsky and M. Sherman, “Home Health Care Patients and Safety Hazards in the Home: Preliminary Findings,” Advances in Patient Safety: New Directions and Alternative Approaches, vol. 1, pp. 1-16, 2008. [3] C. Burghard, “Big Data and Analytics Key to Accountable Care Success,” IDC Health Insights

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Kari Venkatram and Mary A. Geetha

References 1. Demchenko, Y., C. D. Laat, P. Membrey. Defining Architecture Components of the Big Data Ecosystem. – In: Proc. of International Conference Collaboration Technologies and Systems (CTS’14), Vol. 14 , 2014, pp. 104-112. 2. Slavakis, K., G. B. Giannakis, G. Mateos. Modeling and Optimization for Big Data Analytics: (Statistical) Learning Tools for Our Era of Data Deluge. – IEEE Signal Processing Magazine, Vol. 31 , 2014, pp. 18-31. 3. Sherman, R. Chapter 1 – The Business Demand for Data, Information, and Analytics. – Business

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Saadia Karim, Tariq Rahim Soomro and S. M. Aqil Burney

R eferences [1] PWC, “Big Data Analytics - UN Data Innovation Lab 4,” University of Nairobi, Nairobi, 2017. [2] J. Kerber, “Demystifying Big Data: A Practical Guide To Transforming The Business of Government,” pp. 1–40, 2012. [3] McKinsey & Company, “Big data: The next frontier for innovation, competition, and productivity,” McKinsey Glob. Inst., Report, p. 156, 2011. [4] CEBR, “Data equity Unlocking the value of big data,” Report for SAS, pp. 1–44, April 2012. [5] CEBR, “The Value of Big Data and the Internet of Things to the UK

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Claudia Ogrean

5. References Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly , 4 (1), 24-35. Burris, P. (2018). Wikibon’s 2018 Big Data and Analytics Market Share Report . March 6. https://wikibon.com/wikibons-2018-big-data-analytics-market-share-report/ . Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences , 275 , 314-347. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and

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Igor Perko and Peter Ototsky

sourcing practices on the ethical judgments and intentions of US consumers. Journal of Operations Management, 36, 229-243. http://dx.doi.org/10.1016/j.jom.2015.01.001 8. Bryant, E. Randal, Katz, H. Randy, & Lazowska, D. Edward. (2008). Big-Data Computing: Creating revolutionary breakthroughs in commerce, science, and society. http://cra.org/ccc/wp-content/uploads/sites/2/2015/05/Big_Data.pdf 9. Cancer, Vesna, Rebernik, Miroslav, & Knez-Riedl, Jozica. (2013). The environmental creditworthiness assessment methodology/ Metodologija presojanja

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Volker Bosch

References Fenn, Jackie (1995): The Microsoft System Software Hype Cycle Strikes Again Gaffert P., Bosch V., Meinfelder, F. (2016): “Interactions and squares. Don’t transform, just impute!,” Conference Paper, Joint Statistical Meetings, Chicago http://www.ibmbigdatahub.com/infographic/four-vs-big-data http://fivethirtyeight.blogs.nytimes.com/2012/11/10/which-polls-fared-best-and-worst-inthe-2012-presidential-race/?_r=0

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Liang Hong, Mengqi Luo, Ruixue Wang, Peixin Lu, Wei Lu and Long Lu

1 Introduction Big Data, the generic term for data sets of structured and unstructured data that are extremely large and complex so that the traditional software, algorithm, and data repositories are inadequate to collect, process, analyze, and store them ( Asante-Korang & Jacobs, 2016; Kyoungyoung Jee & Gang Hoon Kim, 2013 ; Khoury & Ioannidis, 2014 ; Tan, Gao, & Koch, 2015), has become an intensively studied area in recent years. With the development of the Internet, the mobile Internet, the Internet of things, social media, biology, finance, and digital

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Ogerta Elezaj, Dhimiter Tole and Nevila Baci

References Chen, Jidong & Tao, Ye & Wang, Haoran & Chen, Tao (2015) Big data based fraud risk management at Alibaba, The Journal of Finance and Data Science,1 (1): 1-10. Daas, Piet JH, Marco J. Puts, Bart Bulenes, and Paul AM van den Hurk. “Big Data as a Source for Official Statistics.” Journal of Official Statistics 32, no. 2 (2015): 249-262. Darren & Choo, Kim-Kwang Raymond (2014) Impacts of increasing volume of digital forensic data: A survey and future research challenges, Digital Investigation, 11 (4): 273-294. Desouza, Kevin C. & Jacob