Abstract
Large enterprises and companies often use different tools and systems that distributed across their company branches to operate daily business operations. The collected data and logs have significant potential of providing useful information and insights for the company; however, staffs may spend massive time and effort to process the raw data into useful information as raw data is scattered and distributed across different platforms. This study proposes a framework called Dynamic Composable Analytic Framework (DCAF), which is able to accept and compose raw data from different systems or tools, and performs analytics on the composed data to identify or predict the consumer behavior. The proposed framework is able to perform data receiver, data composition, data massaging and data analytic job with minor human interaction. DCAF provides contribution as an end-to-end solution for converting raw data to predicted customer behavior information and thus improving the customer analytics efficiency.
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References
LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52(2), 21 (2011)
Ji-fan Ren, S., Fosso Wamba, S., Akter, S., Dubey, R., Childe, S.J.: Modelling quality dynamics, business value and firm performance in a big data analytics environment. Int. J. Prod. Res. 55(17), 5011–5026 (2017)
Sodenkamp, M., Kozlovskiy, I., Staake, T.: Gaining is business value through big data analytics: a case study of the energy sector (2015)
Xu, Z., Frankwick, G.L., Ramirez, E.: Effects of big data analytics and traditional marketing analytics on new product success: a knowledge fusion perspective. J. Bus. Res. 69(5), 1562–1566 (2015)
Jagadish, H.V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R., Shahabi, C.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2014)
Ross-Talbot, S.: Orchestration and choreography: Standards, tools and technologies for distributed workflows. In: NETTAB Workshop-Workflows Management: New Abilities for the Biological Information Overflow, Naples, Italy, vol. 1, p. 8, October 2005
Hughes, A.M.: Strategic database marketing. McGraw-Hill Pub, New York (2005)
Coetzee, P., Jarvis, S.A.: Goal-based composition of scalable hybrid analytics for heterogeneous architectures. J. Parallel Distrib. Comput. (2016)
Siriweera, T.H.A.S., Paik, I., Kumara, B.T., Koswatta, K.R.C.: Intelligent big data analysis architecture based on automatic service composition. In: 2015 IEEE International Congress on Big Data (BigData Congress), pp. 276–280. IEEE, June 2015
Benatallah, B., Sheng, Q.Z., Dumas, M.: The self-serv environment for web services composition. IEEE Internet Comput. 7(1), 40–48 (2003)
Chen, P.Y., Hwang, S.Y., Lee, C.H.: A dynamic service composition architecture in supporting reliable web service selection. In: 2013 Fifth International Conference on Service Science and Innovation (ICSSI). IEEE (2013)
Klukas, C., Chen, D., Pape, J.M.: Integrated analysis platform: an open-source information system for high-throughput plant phenotyping. Plant Physiol. 165(2), 506–518 (2014)
Fielder, L.H., Dasey, T.J.: Systems and Methods for Composable Analytics (No. MIT/LL-CA-1). Massachusetts Inst of Tech Lexington Lincoln Lab (2014)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM, April 2010
Derveaux, S., Vandesompele, J., Hellemans, J.: How to do successful gene expression analysis using real-time PCR. Methods 50(4), 227–230 (2010)
What Is Windows Communication Foundation. (n.d.). https://msdn.microsoft.com/en-us/library/ms731082(v=vs.110).aspx. Accessed 14 May 2017
Weske, M.: Business process management architectures. Business Process Management, pp. 333–371. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28616-2_7
Milner, M.: A Developer’s Introduction to Windows Workflow Foundation (WF) in .NET 4, April 2010. https://msdn.microsoft.com/en-us/library/ee342461.aspx. Accessed 13 May 2017
Gupta, S., Lehmann, D.R.: Customers as assets. J. Interact. Mark. 17(1), 9–24 (2003)
Dunford, R., Su, Q., Tamang, E.: The Pareto principle. Plymouth Stud. Sci. 7(1), 140–148 (2014)
Chen, D., Sain, S.L., Guo, K.: Data mining for the online retail industry: a case study of RFM model-based customer segmentation using data mining. J. Database Mark. Cust. Strategy Manag. 19(3), 197–208 (2012)
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Goh, JQ., Chua, FF. (2018). Dynamic Composable Analytics on Consumer Behaviour. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10960. Springer, Cham. https://doi.org/10.1007/978-3-319-95162-1_24
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DOI: https://doi.org/10.1007/978-3-319-95162-1_24
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