A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. (March 2016)

• Record Type: Journal Article
• Authors: de Leoni, Massimiliano; van der Aalst, Wil M.P.; Dees, Marcus
• Is Part Of: Information systems. Volume 56(2016)
• Page Start: 235
• Page End: 257
• Publication Date: 2016-03
• Subjects: Process mining -- Decision and regression trees -- Event-log manipulation -- Event-log clustering; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2015.07.003
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: Process mining can be viewed as the missing link between model-based process analysis and data-oriented analysis techniques. Lion׳s share of process mining research has been focusing on process discovery (creating process models from raw data) and replay techniques to check conformance and analyze bottlenecks. These techniques have helped organizations to address compliance and performance problems. However, for a more refined analysis, it is essential to correlate different process characteristics . For example, do deviations from the normative process cause additional delays and costs? Are rejected cases handled differently in the initial phases of the process? What is the influence of a doctor׳s experience on treatment process? These and other questions may involve process characteristics related to different perspectives (control-flow, data-flow, time, organization, cost, compliance, etc.). Specific questions (e.g., predicting the remaining processing time) have been investigated before, but a generic approach was missing thus far. The proposed framework unifies a number of approaches for correlation analysis proposed in literature, proposing a general solution that can perform those analyses and many more. The approach has been implemented in ProM and combines process and data mining techniques. In this paper, we also demonstrate the applicability using a case study conducted with the UWV (Employee Insurance Agency), one of the largest "administrative factories" in The Netherlands.