An RDR-Based Approach for Event Data Analysis

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 177)

Abstract

Event data analysis is becoming increasingly of interest to academic researchers looking for patterns in the data, contributing to the emergence and popularity of a new field called “data intensive science”. Unlike domain experts working in large companies which have access to IT staff and expensive software infrastructure, researchers find it harder to efficiently manage event processing rules by themselves especially when these rules increase in size and complexity over time. In this paper, we propose an event data analysis platform intended for non-IT experts that facilitates the evolution of event processing rules according to changing requirements. This platform integrates a rule learning framework called Ripple-Down Rules (RDR) operating in conjunction with an event pattern detection process invoked as a service. This solution is demonstrated on real-life scenario involving financial data analysis.

Keywords

Event-based data Event processing Event data model Data intensive science Ripple down rules 

Notes

Acknowledgement

We would like to thank the Smart Services Cooperative Research Centre in Australia for sponsoring our research project and Sirca for providing financial data used in the case study. We would also thank Prof. Paul Compton for his valuable advice on the RDR technique.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia

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