Encyclopedia of Social Network Analysis and Mining

2018 Edition
| Editors: Reda Alhajj, Jon Rokne

Desire Lines in Big Data

  • Wil M. P. van der AalstEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-7131-2_396

Synonyms

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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Notes

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/).

References

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Recommended Reading

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

Copyright information

© Springer Science+Business Media LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands

Section editors and affiliations

  • V. S. Subrahmanian
    • 1
  • Jeffrey Chan
    • 2
  1. 1.University of MarylandCollege ParkUSA
  2. 2.RMIT (Royal Melbourne Institute of Technology)MelbourneAustralia