An RDR-Based Approach for Event Data Analysis
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.
KeywordsEvent-based data Event processing Event data model Data intensive science Ripple down rules
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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