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Spatiotemporal Aspects of Big Data

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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Relevance of Big Data for Business and Management. Exploratory Insights (Part I)

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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Big Data Analysis of Home Healthcare Services

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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Review on Big Data & Analytics – Concepts, Philosophy, Process and Applications

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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Exploring complex and big data

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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Relevance of Big Data for Business and Management. Exploratory Insights (Part II)

5. References Bean, R. (2018). How Big Data and AI are driving business innovation in 2018 . MIT Sloan Management Review Webinar. http://newvantage.com/wp-content/uploads/2018/07/MIT-Bean-Webinar-2018-slides-FINAL-071818.pdf . 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/ . Davenport, T. H., Barth, P., & Bean, R. (2012). How’big data’is different . MIT Sloan Management Review. July 30. Dresner Advisory Services, LLC

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Big Data for Business Ecosystem Players

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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Big Data in Health Care: Applications and Challenges

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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Structural Ageism in Big Data Approaches

neural networks. In Machine learning, optimization, and big data (MOD 2017) (pp. 337-348). Volterra: Springer. Bi, B., Shokouhi, M., Kosinski, M. & Graepel, T. (2013). Inferring the demographics of search users: Social data meets search queries. In Conference on World Wide Web (WWW’13) (pp. 131-140) Rio de Janeiro: ACM Press. Bijker, W. E., Hughes, T. P. & Pinch, T. J. (eds.) (1989). The social construction of technological systems . London: MIT Press. Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to

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Big Data in e-Government Environments: Albania as a Case Study

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

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