Applications and Practices of Big Data for Development
DOI:
https://doi.org/10.18034/abr.v7i3.597Keywords:
Application Development, Human Development, Big Data, Big Data AnalyticsAbstract
Daily, vast volumes of public data are generated due to the expansion of social media sites, digital computing devices, and Internet connectivity. Effective data analysis techniques/algorithms can provide near real-time knowledge regarding emerging patterns and early warning in case of an emergency (such as the outbreak of a viral disease). These data can also disclose several helpful indicators of socioeconomic and political events that can assist formulate effective public policy. This study examines the use of big data analytics for human development. As big data technology matures, it will be possible to use it for development purposes, such as addressing humanitarian crises or violent conflicts. The large-scale use of big data for development is fraught with obstacles due to its huge quantity, rapid change, and diversity. The most urgent challenges are effective data collection and exchange, providing context (e.g., geolocation and time), and ensuring data accuracy and privacy. This study reviews existing big data for development studies to assess the impact of big data on society's development. We examine major efforts while also highlighting obstacles and unresolved issues.
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Adusumalli, H. P. (2016a). Digitization in Production: A Timely Opportunity. Engineering International, 4(2), 73-78. https://doi.org/10.18034/ei.v4i2.595
Adusumalli, H. P. (2016b). How Big Data is Driving Digital Transformation?. ABC Journal of Advanced Research, 5(2), 131-138. https://doi.org/10.18034/abcjar.v5i2.616
Adusumalli, H. P. (2017a). Mobile Application Development through Design-based Investigation. International Journal of Reciprocal Symmetry and Physical Sciences, 4, 14–19. Retrieved from https://upright.pub/index.php/ijrsps/article/view/58
Adusumalli, H. P. (2017b). Software Application Development to Backing the Legitimacy of Digital Annals: Use of the Diplomatic Archives. ABC Journal of Advanced Research, 6(2), 121-126. https://doi.org/10.18034/abcjar.v6i2.618
Eagle, N., Pentland, A. (2006). Reality mining: sensing complex social systems. Pers Ubiquitous Comput, 10(4), 255–68.
Kshetri, N. (2014). The emerging role of big data in key development issues: Opportunities, challenges, and concerns. Big Data & Society, 1(2), 2053951714564227.
Laurila, J. K., Gatica-Perez, D., Aad, I., Blom, J., Bornet, O., Do, T-M-T., Dousse, O., Eberle, J., Miettinen, M. (2012). The mobile data challenge: Big data for mobile computing research. In: Proceedings of the Workshop on the Nokia Mobile Data Challenge, in Conjunction with the 10th International Conference on Pervasive Computing, p. 1–8.
Leavitt, N. (2010). Will nosql databases live up to their promise?. Computer, 43(2), 12–4.
Pasupuleti, M. B. (2015a). Data Science: The Sexiest Job in this Century. International Journal of Reciprocal Symmetry and Physical Sciences, 2, 8–11. Retrieved from https://upright.pub/index.php/ijrsps/article/view/56
Pasupuleti, M. B. (2015b). Problems from the Past, Problems from the Future, and Data Science Solutions. ABC Journal of Advanced Research, 4(2), 153-160. https://doi.org/10.18034/abcjar.v4i2.614
Pasupuleti, M. B. (2015c). Stimulating Statistics in the Epoch of Data-Driven Innovations and Data Science. Asian Journal of Applied Science and Engineering, 4, 251–254. Retrieved from https://upright.pub/index.php/ajase/article/view/55
Pasupuleti, M. B. (2016a). The Use of Big Data Analytics in Medical Applications. Malaysian Journal of Medical and Biological Research, 3(2), 111-116. https://doi.org/10.18034/mjmbr.v3i2.615
Pasupuleti, M. B. (2016b). Data Scientist Careers: Applied Orientation for the Beginners. Global Disclosure of Economics and Business, 5(2), 125-132. https://doi.org/10.18034/gdeb.v5i2.617
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Netw, 61, 85–117.
Shmueli, G., Koppius, O. (2010). Predictive analytics in information systems research: Robert H. Smith School Research Paper No. RHS, 06–138.
Tufte, E. R. (1983). The Visual Display of Quantitative Information. CT: Graphics press Cheshire. Graves-Morris P, Vol. 2.
Witten, I. H., Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques: Morgan Kaufmann.
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Copyright (c) 2017 Harshini Priya Adusumalli, Mahesh Babu Pasupuleti
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