An RDR-Based Approach for Event Data Analysis

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


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.


Event-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.


  1. 1.
    Luckham, D.: The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison Wesley Professional, Reading (2002)Google Scholar
  2. 2.
    Luckham, D.: What’s the Difference Between ESP and CEP? (2006).
  3. 3.
    Rabhi, F., Yao, L., Guabtni, A.: ADAGE: a framework for supporting user-driven ad hoc data analysis processes. Computing 94, 489–519 (2012)CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Etzion, O., Niblett, P.: Event Processing in Action. Manning Publications Co., Stamford (2011)Google Scholar
  6. 6.
    Cugola, G., Margara, A.: Processing flows of information: from data stream to complex event processing. ACM Comput. Surv. 44, 1–62 (2012)CrossRefGoogle Scholar
  7. 7.
    Chandy, K., Schulte, W.: Event Processing: Designing IT Systems for Agile Companies. McGraw-Hill, New York (2010)Google Scholar
  8. 8.
    Hinze, A., Sachs, K., Buchmann, A.: Event-based applications and enabling technologies. In: Proceedings of the Third ACM International Conference on Distributed Event-Based Systems, Nashville, Tennessee (2009)Google Scholar
  9. 9.
    Sen, S., Stojanovic, N.: GRUVe: a methodology for complex event pattern life cycle management. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 209–223. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Obweger, H., Schiefer, J., Suntinger, M., Kepplinger, P., Rozsnyai, S.: User-Oriented Rule Management for Event-Based Applications. In: Proceedings of the Fifth ACM International Conference on Distributed Event-Based System, New York, USA (2011)Google Scholar
  11. 11.
    Richards, D.: Two decades of ripple down rules research. Knowl. Eng. Rev. 24(2), 159–184 (2009)CrossRefGoogle Scholar
  12. 12.
    Compton, P., Peters, L., Edwards, G., Lavers, T.G.: Experience with ripple-down rules. Knowl. Based Syst. 19, 356–362 (2006)CrossRefGoogle Scholar
  13. 13.
    Rabhi, F.A., Chen, W., Perry, R., Yao, L., Natarajan A.: A new data model for representing events and event patterns. Internal Report, Service Oriented Computing Research Group, School of Computer Science and Engineering, University of New South Wales (2013)Google Scholar
  14. 14.
    Kang, B.H., Compton, P., Preston, P.: Multiple classification ripple down rules: evaluation and possibilities. In: The Ninth Banff Knowledge Acquisition for Knowledge Based Systems Workshop (1995)Google Scholar
  15. 15.
    Prasad, K.H., Faruquie, T.A., Joshi, S., Chaturvedi, S., Subramaniam, L.V., Mohania M.: Data cleansing techniques for large enterprise datasets. In: Annual SRII Global Conference (SRII), pp. 135–144 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

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

Personalised recommendations