A real-time business intelligence system based on the ACP approach

Abstract

In today's competitive environment, it is important to analyze data to predict market trends and to improve enterprise performances. The market competition and products upgrading are so vigorous that business success requires the data analysis to be carried out in real-time, or at least, as fast as possible. The real time analysis can help make decisions in time, and that actions in response to the analysis results must also be performed fast enough to meet the rapid changes in the demands from customers and regulators. The ACP (Artificial systems/societies, Computational experiments and Parallel execution) approach has provided us an opportunity to use various methods in modeling and analyzing a complex system in real time. We can build up artificial systems to reflect how the actual systems could be running. Computational experiments are performed to analyze different scenarios or different configurations of the system. The parallel execution provides a mechanism for using the results from the experiments on the artificial systems to guide the real system. In this paper, we present our approach of applying ACP to establishing a real-time business intelligence system. Compared with traditional business intelligence systems, the ACP approach provides a variety of possible results by scientific calculations in advance, which can help make real time or at least fast enough decisions.