Abstract
Event data analysis is becoming increasingly of interest to academic researchers looking for patterns in the data. Unlike domain experts working in large companies who have access to IT staff and expensive software infrastructures, researchers find it harder to efficiently manage their event data analysis by themselves. Particularly, user-driven rule management is a challenge especially when analysis rules increase in size and complexity over time. In this paper, we propose an event data analysis platform called EP-RDR 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 component invoked as a service (EPDaaS). We have built a prototype to demonstrate this solution on real-life scenario involving financial data analysis.
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Notes
“epsAmount” denotes the value of the field “EPS Amount” in the “Earning” event, and “EPS_scaling_factor” denotes the value of the field “EPS Scaling Factor”
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Acknowledgments
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
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Chen, W., Rabhi, F.A. Enabling user-driven rule management in event data analysis. Inf Syst Front 18, 511–528 (2016). https://doi.org/10.1007/s10796-016-9633-2
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DOI: https://doi.org/10.1007/s10796-016-9633-2