A Service Oriented Analytics Framework for Multi-Level Marketing Business

Rich C. Lee

System Technology Group, IBM, Taipei, Chinese Taipei.

DOI: 10.4236/jsea.2012.58061                

Abstract

Today’s enterprises have accumulated vast amount of data and keep exploding by business activities. These datasets may contain potential undiscovered business strategies as a key basis of competition; underpin new waves of productivity growth, innovation, and consumer surplus. Data analysis is crucial in making managerial decisions. Although there are many Business Intelligence (BI) software of commercial and open source, but serving statistical purpose as exuberant as GNU-R (R) is rare. R is a highly extensible language and environment for providing a variety of statistical and graphical features. In enterprise environment, the source data are stored in various forms such as files, database, and streaming data. Currently analysts conduct data analysis in offline mode using statistical software. The offline mode means analysts 1) extract the desired data; 2) store extracted data into files; 3) manipulate software; 4) draw analytical results; 5) generate the inferences. Automating the statistical procedures by directly pulling source data will make critical decision sooner and less costly. It is a common practice that enterprise adopts Service-Oriented Architecture (SOA) to achieve its operation excellence. Since the business applications populate the source data during the operation processes, pulling the source data directly under SOA is the most effective way of data analysis. This paper demonstrates how service-oriented statistics engine was developed and how such a system benefits the business decision-making.

Keywords

Software Engineering; SOA; Business Analytics; Big Data; GNU-R

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	B. P. Kumar, J. Selvam, V. Meenakshi, K. Kanthi, A. Suseela and V. L. Kumar, “Business Decision Making, Management and Information Technology,” Ubiquity, Vol. 2007, 2007, p. 4. doi:10.1145/1226690.1232401
[2]	H. Arsham, “Statistical Thinking for Managerial Decisions,” 9th Edition, 2011. http://home.ubalt.edu/ntsbarsh/Business-stat/opre504.htm
[3]	R. N. Charette, “Why Software Fails,” IEEE Spectrum, Vol. 42, No. 9, 2005, p. 36. doi:10.1109/MSPEC.2005.1502528
[4]	M. T. Schmidt, B. Hutchison, P. Lambros and R. Phippen, “The Enterprise Service Bus: Making Service-Oriented Architecture Real,” IBM Systems Journal, Vol. 44, No. 4, 2005, pp. 781-797. doi:10.1147/sj.444.0781
[5]	J. L. Maréchaux, “Combining Service-Oriented Architecture and Event-Driven Architecture Using an Enterprise Service Bus,” IBM Developer Works, 2006.
[6]	Y. Wei, Z. Sun, X. Chen and F. Zhang, “A Service-Port-let Based Visual Paradigm for Personalized Convergence of Information Resources,” 2nd IEEE International Conference on Computer Science and Information Technology, Beijing, 8-11 August 2009.
[7]	L. Chung and J. do Prado Leite, “On Non-Functional Requirements in Software Engineering,” Conceptual modeling: Foundations and Applications, Vol. 5600, 2009, pp. 363-379,.
[8]	B. Behkamal, M. Kahani and M. K. Akbari, “Customizing ISO 9126 Quality Model for Evaluation of B2B Applications,” Information and Software Technology, Vol. 51, No. 3, 2009, pp. 599-609. doi:10.1016/j.infsof.2008.08.001
[9]	A. T. Yalta and R. Lucchetti, “The GNU/Linux Platform and Freedom Respecting Software for Economists,” Journal of Applied Econometrics, Vol. 23, No. 2, 2008, pp. 279-286. doi:10.1002/jae.990
[10]	M. Lapsley and B. Ripley, “ODBC Database Access,” 2011. http://cran.r-project.org/web/packages/RODBC/RODBC.pdf
[11]	A. Friis-Christensen, N. Ostlander, M. Lutz and L. Bernard, “Designing Service Architectures for Distributed Geoprocessing: Challenges and Future Directions,” Transactions in GIS, Vol. 11, No. 6, 2007, pp. 799-818. doi:10.1111/j.1467-9671.2007.01075.x
[12]	C. F. Liao, Y. W. Jong and L. C. Fu, “Toward a Mes-sage-Oriented Application Model and Its Middleware Support in Ubiquitous Environments,” Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering, IEEE Computer Society, Washington, 2008, pp. 298-303.doi:10.1109/MUE.2008.24
[13]	F. Menge, “Enterprise Service Bus,” 2007.
[14]	A. Vaccaro, R. Parente and F. M. Veloso, “Knowledge Management Tools, Inter-Organizational Relationships, Innovation and Firm Performance,” Technological Forecasting and Social Change, Vol. 77, No. 7, 2010, pp. 1076-1089. doi:10.1016/j.techfore.2010.02.006
[15]	U. R. Kulkarni, S. Ravindran and R. Freeze, “A Knowledge Management Success Model: Theoretical Development and Empirical Validation,” Journal of Management Information Systems, Vol. 23, No. 3, 2007, pp. 309-347. doi:10.2753/MIS0742-1222230311