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Desire Lines in Big Data

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Encyclopedia of Social Network Analysis and Mining

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Synonyms

Business process intelligence; Distributed process mining; Process discovery; Process mining

Glossary

Conformance checking:

Monitoring deviations by comparing model and log

Event log:

Multiset of traces

Event:

Occurrence of some discrete incident (e.g., completion of an activity)

Process discovery:

Extracting process models from an event log

Process mining:

Collection of techniques to discover, monitor, and improve real processes by extracting knowledge from event data

Trace:

Sequence of events

Definition

Processes leave footprints in information systems just like people leave footprints in grassy spaces. Desire lines, i.e., the tracks formed by erosion showing where people really walk, may be very different from the formal pathways. When people deviate from the official path there is often a good reason and room for improvement. The goal of process mining is to extract desire lines from event logs, e.g., to automatically infer a process model from raw events recorded by some...

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Recommended Reading

  • To get started with process mining, the reader is advised to read the book “Process mining: data science in action” (van der Aalst 2016)

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Acknowledgments

The author would like to thank all involved in the development of the process mining tool ProM and related techniques (processmining.org) and all members of the IEEE Task Force on Process Mining (www.win.tue.nl/ieeetfpm/).

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Eindhoven University of Technology, PO Box 513, NL-5600 MB, Eindhoven, The Netherlands

    Wil M. P. van der Aalst

Authors
  1. Wil M. P. van der Aalst
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Corresponding author

Correspondence to Wil M. P. van der Aalst .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jon Rokne

Section Editor information

  1. University of Maryland, College Park, MD, USA

    V. S. Subrahmanian

  2. RMIT (Royal Melbourne Institute of Technology), Melbourne, Australia

    Jeffrey Chan

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van der Aalst, W.M.P. (2018). Desire Lines in Big Data. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_396

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