Profiling event logs to configure risk indicators for process delays

Authors:
Anastasiia Pika;Wil M. P. Van Der Aalst;Colin J. Fidge;Arthur H. M. Ter Hofstede;Moe T. Wynn
Affiliations:
Queensland University of Technology, Brisbane, Australia;Eindhoven University of Technology, Eindhoven, The Netherlands,Queensland University of Technology, Brisbane, Australia;Queensland University of Technology, Brisbane, Australia;Queensland University of Technology, Brisbane, Australia,Eindhoven University of Technology, Eindhoven, The Netherlands;Queensland University of Technology, Brisbane, Australia
Venue:
CAiSE'13 Proceedings of the 25th international conference on Advanced Information Systems Engineering
Year:
2013

Citing 11
Cited 0

Improving Business Process Quality through Exception Understanding, Prediction, and Prevention

Proceedings of the 27th International Conference on Very Large Data Bases
Business process intelligence

Computers in Industry - Special issue: Process/workflow mining
Workflow Mining: Discovering Process Models from Event Logs

IEEE Transactions on Knowledge and Data Engineering
Operational risk analysis in business processes

BT Technology Journal
Cycle Time Prediction: When Will This Case Finally Be Finished?

OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
A study of cross-validation and bootstrap for accuracy estimation and model selection

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Time prediction based on process mining

Information Systems
Process Mining: Discovery, Conformance and Enhancement of Business Processes

Process Mining: Discovery, Conformance and Enhancement of Business Processes
A business process mining application for internal transaction fraud mitigation

Expert Systems with Applications: An International Journal
A framework for risk assessment based on analysis of historical information of workflow execution in IT systems

Computer Networks: The International Journal of Computer and Telecommunications Networking
Process Mining Put into Context

IEEE Internet Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators (PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company.