International Journal of Computer Networks and Applications

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A Framework for Effective Big Data Analytics for Decision Support Systems


Affiliations
1 Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia
 

Supporting decision makers requires a good understanding of the various elements that affect the outcomes of a decision. Decision Support Systems have provided decision makers with such insights throughout its history of usage with varying degrees of success. The availability of data sources was a main limitation to what decision support systems can do. Therefore, with the advent of improved analytical methods for Big data sources new opportunities have emerged that can possibly enhance how decision makers analyze their problem and arrive at decisions using information systems. This paper analyzed current related works on both Big data and decision support systems to identify clear elements and factors relevant to the subject and identifying possible ways to enhance their joint usage. Finally, the paper proposes a framework that integrates the key components needed to ensure the quality and relevance of data being analyzed by decision support systems while providing the benefits of insights generated over time from past decisions and positive recommendations.

Keywords

Big Data, Big Data Analytics, Decision Support Systems, Information Systems.
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  • Bhargava, H. and Power, D. (2001) ‘Decision Support Systems and Web Technologies: A Status Report’, Americas Conference on Information Systems 2001 Proceedings, Paper 46.

  • Laudon, K. and Laudon, J. (2006) Management Information Systems: Managing the Digital Firm, 9th Edition, Upper Saddle River New Jersey, Pearson – Prentice Hall.

  • Power, D. J. (2004). Specifying an Expanded Framework for Classifying and Describing Decision Support Systems. The Communications of the Association for Information Systems, 13(1), 52.

  • Sagiroglu, S., & Sinanc, D. (2013). Big data: A review. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.

  • Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data analytics: a survey. Journal of Big data, 2(1), 1-32.

  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big data to Big Impact. MIS quarterly, 36(4), 1165-1188.

  • Renu, R. S., Mocko, G., & Koneru, A. (2013). Use of Big data and knowledge discovery to create data backbones for decision support systems. Procedia Computer Science, 20, 446-453.

  • Poleto, T., de Carvalho, V. D. H., & Costa, A. P. C. S. (2015). The Roles of Big data in the Decision-Support Process: An Empirical Investigation. In Decision Support Systems V–Big data Analytics for Decision Making (pp. 10-21). Springer International Publishing.

  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management,35(2), 137-144.

  • Phillips-Wren, G., & Hoskisson, A. (2015). An analytical journey towards Big data. Journal of Decision Systems, 24(1), 87-102.

  • Oztayşi¸ B., Kaya, T., & Kahraman, C. (2011). Performance comparison based on customer relationship management using analytic network process. Expert Systems with Applications, 38, 9788–9798.

  • Ludwig, N., Feuerriegel, S., & Neumann, D. (2015). Putting Big data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests. Journal of Decision Systems, 24(1), 19-36.

  • Amin Y. Noaman, Farrukh Nadeem, Abdul Hamid M. Ragab, et al., “Improving Prediction Accuracy of “Central Line-Associated Blood Stream Infections” Using Data Mining Models,” BioMed Research International, vol. 2017, Article ID 3292849, 12 pages, 2017. doi:10.1155/2017/3292849.

  • Clarke, R. (2016). Big data, big risks. Information Systems Journal, 26(1), 77-90.

  • Manal Abumelha, Awatef Hashbal, Farrukh Nadeem, Naif Aljohani, Development of Infection Control Surveillance System for Intensive Care Unit: Data Requirements and Guidelines, International Journal of Intelligent Systems and Applications 8(6): 19-26, May 2016.

  • Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: Big data and the changing context of strategy. Journal of Information Technology,30(1), 44-57.

  • Pick, R. A., & Weatherholt, N. (2013). A Review On Evaluation and Benefits of Decision Support Systems. The Review of Business Information Systems (Online), 17(1), 7.

  • Eom, S. and Kim, E. (2006). A Survey of Decision Support System Applications (1995-2001). The Journal of the Operational Research Society, 57(11), 1264–1278.

  • Marx, V. (2013). The Big Challenges of Big data. Nature, 498(7453), 255-60.

  • Lokhande, S., and Khare, N. (2015). An outlook on Big data and Big data analytics. International Journal of Computer Applications, 124(11).

  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.

  • Ohlhorst, F, (2012) “Best Practices for Big Data Analytics,” in Big Data Analytics, John Wiley & Sons, Inc., pp. 93–109.

  • Power, D. J. (2014). Using ‘Big data’for analytics and decision support. Journal of Decision Systems, 23(2), 222-228.

  • Deloitte (2016). Analytics Trends 2016: The Next Evolution [Online]. Available from: http://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/analytics-trends.html (Accessed: 28/4/2016).

  • Khan, Z.A. and Samad, A., 2017. A Study of Machine Learning in Wireless Sensor Network. International Journal of Computer Networks and Applications (IJCNA), 4(4).

  • Vancin, S. and Erdem, E., 2015. Design and simulation of wireless sensor network topologies using the ZigBee standard. International Journal of Computer Networks and Applications (IJCNA), 2(3), pp.135-143.

  • Russom, P. (2011). Big data analytics. TDWI Best Practices Report, Fourth Quarter, 1-35.

  • Ittmann, H. W. (2015). The impact of Big data and business analytics on supply chain management. Journal of Transport and Supply Chain Management, 9(1), 9-pages.


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  • A Framework for Effective Big Data Analytics for Decision Support Systems

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Authors

Osama Islam
Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia
Ahmed Alfakeeh
Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia
Farrukh Nadeem
Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia

Abstract


Supporting decision makers requires a good understanding of the various elements that affect the outcomes of a decision. Decision Support Systems have provided decision makers with such insights throughout its history of usage with varying degrees of success. The availability of data sources was a main limitation to what decision support systems can do. Therefore, with the advent of improved analytical methods for Big data sources new opportunities have emerged that can possibly enhance how decision makers analyze their problem and arrive at decisions using information systems. This paper analyzed current related works on both Big data and decision support systems to identify clear elements and factors relevant to the subject and identifying possible ways to enhance their joint usage. Finally, the paper proposes a framework that integrates the key components needed to ensure the quality and relevance of data being analyzed by decision support systems while providing the benefits of insights generated over time from past decisions and positive recommendations.

Keywords


Big Data, Big Data Analytics, Decision Support Systems, Information Systems.

References