An Approach to Identifying False Traces in Process Event Logs

By means of deriving knowledge from event logs, the application of process mining algorithms can provide valuable insight into the actual execution of business processes and help identify opportunities for their improvement. The event logs may be collected by people manually or generated by a variety of software applications, including business process management systems. However logging may not always be done in a reliable manner, resulting in events being missed or interchanged. Consequently, the results of the application of process mining algorithms to such “polluted” logs may not be so reliable and it would be preferable if false traces, i.e. polluted traces which are not possibly valid as regards the process model to be discovered, could be identified first and removed before such algorithms are applied. In this paper an approach is proposed that assists with identifying false traces in event logs as well as the cause of their pollution. The approach is empirically validated.

[1]  Wil M. P. van der Aalst,et al.  Process Mining: Overview and Opportunities , 2012, ACM Trans. Manag. Inf. Syst..

[2]  Hui Xiong,et al.  Enhancing data analysis with noise removal , 2006, IEEE Transactions on Knowledge and Data Engineering.

[3]  Alexander L. Wolf,et al.  Discovering models of behavior for concurrent workflows , 2004, Comput. Ind..

[4]  Wil M. P. van der Aalst,et al.  Rediscovering workflow models from event-based data using little thumb , 2003, Integr. Comput. Aided Eng..

[5]  Wil M. P. van der Aalst,et al.  A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs , 2005, Data Mining and Knowledge Discovery.

[6]  Guido Governatori,et al.  Compliance aware business process design , 2008 .

[7]  Wil M. P. van der Aalst,et al.  Process Aware Information Systems: Bridging People and Software Through Process Technology , 2005 .

[8]  Boudewijn F. van Dongen,et al.  Process Mining: Overview and Outlook of Petri Net Discovery Algorithms , 2009, Trans. Petri Nets Other Model. Concurr..

[9]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .

[10]  Kees M. van Hee,et al.  Workflow Management: Models, Methods, and Systems , 2002, Cooperative information systems.

[11]  Wil M. P. van der Aalst,et al.  Transactions on Petri Nets and Other Models of Concurrency II, Special Issue on Concurrency in Process-Aware Information Systems , 2009, Trans. Petri Nets and Other Models of Concurrency.

[12]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[13]  Martin J. Wainwright,et al.  Image denoising using scale mixtures of Gaussians in the wavelet domain , 2003, IEEE Trans. Image Process..

[14]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[15]  Ricardo Seguel,et al.  Process Mining Manifesto , 2011, Business Process Management Workshops.

[16]  Erhard Rahm,et al.  Data Cleaning: Problems and Current Approaches , 2000, IEEE Data Eng. Bull..

[17]  Wil M. P. van der Aalst,et al.  Genetic process mining: an experimental evaluation , 2007, Data Mining and Knowledge Discovery.