Process Mining in the Large: A Tutorial

Chapter
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 172)

Abstract

Recently, process mining emerged as a new scientific discipline on the interface between process models and event data. On the one hand, conventional Business Process Management (BPM) and Workflow Management (WfM) approaches and tools are mostly model-driven with little consideration for event data. On the other hand, Data Mining (DM), Business Intelligence (BI), and Machine Learning (ML) focus on data without considering end-to-end process models. Process mining aims to bridge the gap between BPM and WfM on the one hand and DM, BI, and ML on the other hand. Here, the challenge is to turn torrents of event data (“Big Data”) into valuable insights related to process performance and compliance. Fortunately, process mining results can be used to identify and understand bottlenecks, inefficiencies, deviations, and risks. This tutorial paper introduces basic process mining techniques that can be used for process discovery and conformance checking. Moreover, some very general decomposition results are discussed. These allow for the decomposition and distribution of process discovery and conformance checking problems, thus enabling process mining in the large.

Keywords

Process mining Big Data Process discovery Conformance checking 

Notes

Acknowledgements

This work was supported by the Basic Research Program of the National Research University Higher School of Economics (HSE).

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Business Process Management DisciplineQueensland University of TechnologyBrisbaneAustralia
  3. 3.International Laboratory of Process-Aware Information SystemsNational Research University Higher School of EconomicsMoscowRussia

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