A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs

Research output: Contribution to journalArticleAcademicpeer-review

69 Citations (Scopus)
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Abstract

Process mining can be viewed as the missing link between model-based process analysis and data-oriented analysis techniques. Lions 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 doctors 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.

Original languageEnglish
Pages (from-to)235-257
Number of pages23
JournalInformation Systems
Volume56
DOIs
Publication statusPublished - 1 Mar 2016

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Insurance
Flow control
Data mining
Industrial plants
Costs
Personnel
Processing
Compliance

Keywords

  • Decision and regression trees
  • Event-log clustering
  • Event-log manipulation
  • Process mining

Cite this

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title = "A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs",
abstract = "Process mining can be viewed as the missing link between model-based process analysis and data-oriented analysis techniques. Lions 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 doctors 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.",
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A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. / De Leoni, M.; van der Aalst, W.M.P; Dees, M.

In: Information Systems, Vol. 56, 01.03.2016, p. 235-257.

Research output: Contribution to journalArticleAcademicpeer-review

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