MODEL OF THE IMPLEMENTATION OF THE ELECTRONIC PAYMENT SYSTEMS BASED ON THE BUSINESS INTELLIGENCE

Muzafer Saračević, Sead Mašović

Abstract


This paper describes the concepts and methodologies of business intelligence system that enables users to improve the e-payment service quality and business improvement. The aim is to demonstrate that the use of business intelligence combined with e-payment systems may lead to a better understanding of customers and improve the monitoring and analysis of their behavior. These analyzes can be used to improve customer relations and to gain a competitive advantage over other systems. In this paper, we gave the proposal of integration with the advanced tools and technologies (OLAP, Data Mart and CRM analysis) which would help in shaping the processes that collect data and transform it into useful information and knowledge. Practical part of the paper is implementation of the model of e-payment with elements of business intelligence using advanced Java technologies. The advantages of this type of implementation are given in the last segment of the paper.

Keywords


business intelligence, ePayment, OLAP, Data Mart, Java technology

Full Text:

PDF (Serbian)

References


Fernandez, A., Del Rio, S., Herrera, F., & Benitey, J. (2013). An Overview on the Structure and Applications for BI and Data Mining in Cloud Computing. 7th Inter. Conf. on Knowledge Management in Organizations, Advances in Intelligent Systems and Computing (s. 559-570). Berlin: Springer-Verlag.

Kantardzic, M. (2003). Data Mining: Concepts, Models, Methods, and Algorithms. Wiley-IEEE Press.

Kudelic, K. (2004). Unapredenje poslovanja u sustavima elektronickog placanja. Fakultet elektrotehnike i racunarstva, Sveucilište u Zagrebu.

Oyku, I., Mary, C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information & Management , 50 (1), 13-23.

Paunović, L., Grubić, G., Stokić, A., Popović, S., & Milentijević, D. (2013). Developing business intelligence model for scientific research project management. Metalurgia International , 18 (sp.4), 44-49.

Ross, J., Weill, P., & Robertson, D. (2006). Enterprise Architecture As Strategy, Creating a Foundation for Business Execution. H. Business Review Press.

Sabherwal, R., & Becerra-Fernandez, I. (2010). Business Intelligence. John Wiley & Sons.

Saračević, M., & Mašović, S. (2013). Advantages of ACID compliance in application development in FIREBIRD databases. International Journal of Strategic Management and Decision Support Systems , 18 (1), 53-61.

Saračević, M., Mašović, S., Kamberović, H., & Lončarević, Z. (2010). Programiranje transakcija i uskladištenih procedura u oblasti informacionih sistema. XIV Konferencija : E-Government - Informacioni sistem državnih organa Republike Srbije (IS-DoS).

Saračević, M., Mašović, S., Medjedović, E., Kamberović, H., & Lončarević, Z. (2011). Development of Information Systems in the Database Firebird. The 7th International Conference Research and Development of Mechanical Elements and System-IRMES, (s. 593-598).

Serrano-Cinca, C., & Gutierrez-Nieto, B. (2013). A decision support system for financial and social investment. Applied Economics , 45 (28), 4060-4070.

Taeil, P., & Hyoungkwan, K. (2013). A data Warehouse based decision support system for sewer infrastructure management. Automation In Construction , 30, 37-49.

Usman, M., Pears, R., & Fong, A. (2013). A data mining approach to knowledge discovery from multidimensional cube structures. Knowledge-Based Systems , 40, 36-49.

Vodapalli, T. (2009). Critical Success Factors of BI Implementation. IT University of Copenhagen.

Zhu, X., & Davidson, I. (2007). Knowledge Discovery and Data Mining, Challenges and Realities. IGI Global.


Refbacks

  • There are currently no refbacks.