Framework for process discovery from sensor data

Agnes Koschmider, Dominik Janssen, Felix Mannhardt

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
6 Downloads (Pure)

Abstract

Process mining can give valuable insights into how real-life activities are performed when extracting meaningful activities instances from raw sensor events. However, in many cases, the event data generated during the execution of a process is at a much lower level of abstraction, and, in some cases, even continuous, e.g., sensor data. This paper presents a framework to discover activities and process models from event location sensor data. The framework is flexible enough to be applied to any data set from raw sensor data.

Original languageEnglish
Title of host publicationEMISA 2020 : Enterprise Modeling and Information Systems Architectures 2020
Subtitle of host publication10th International Workshop on Enterprise Modeling and Information Systems Architectures
EditorsAgnes Koschmider, Judith Michael, Bernhard Thalheim
PublisherCEUR-WS.org
Pages32-38
Number of pages7
Publication statusPublished - 2020
Externally publishedYes
Event10th International Workshop on Enterprise Modeling and Information Systems Architectures, EMISA 2020 - Kiel, Germany
Duration: 14 May 202015 May 2020

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS.org
Volume2628
ISSN (Print)1613-0073

Conference

Conference10th International Workshop on Enterprise Modeling and Information Systems Architectures, EMISA 2020
Country/TerritoryGermany
CityKiel
Period14/05/2015/05/20

Keywords

  • Event Abstraction
  • Framework
  • Process Mining
  • Raw Sensor Data

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