Integrating NoSQL, Relational Database, and the Hadoop Ecosystem in an Interdisciplinary Project involving Big Data and Credit Card Transactions

Author(s):  
Romulo Alceu Rodrigues ◽  
Lineu Alves Lima Filho ◽  
Gildarcio Sousa Gonçalves ◽  
Lineu F. S. Mialaret ◽  
Adilson Marques da Cunha ◽  
...  
2018 ◽  
Vol 30 (3) ◽  
pp. 3-22
Author(s):  
Won-Seop Shim ◽  
Seung-Mook Choi ◽  
Chang-Sup Shim

Author(s):  
Effy Vayena ◽  
Lawrence Madoff

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.


Bank marketers still have difficulties to find the best implementation for credit card promotion using above the line, particularly based on customers preferences in point of interest (POI) locations such as mall and shopping center. On the other hand, customers on those POIs are keen to have recommendation on what is being offered by the bank. On this paper we propose a design architecture and implementation of big data platform to support bank’s credit card’s program campaign that generating data and extracting topics from Twitter. We built a data pipeline that consist of a Twitter streamer, a text preprocessor, a topic extractor using Latent Dirichlet Allocation, and a dashboard that visualize the recommendation. As a result, we successfully generate topics that related to specific location in Jakarta during some time windows, that can be used as a recommendation for bank marketers to create promotion program for their customers. We also present the analysis of computing power usages that indicates the strategy is well implemented on the big data platform.


2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Sara Landset ◽  
Taghi M. Khoshgoftaar ◽  
Aaron N. Richter ◽  
Tawfiq Hasanin

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