Clustering Raw Sensor Data in Process Logs to Detect Data Streams

Matthias Ehrendorfer, Juergen Mangler, Stefanie Rinderle-Ma

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

Abstract

The execution and analysis of processes is strongly influenced by sensor streams, e.g., temperature, that are measured in parallel to the process execution and stored in process event logs. This holds particularly true for application domains such as logistics and manufacturing. However, currently, these sensor streams are collected and stored in an arbitrary and unsystematic way. Hence, this work proposes an approach that prepares sensor streams into individual data streams that can be annotated to process tasks and used for process analysis and prediction.

Original languageEnglish
Title of host publicationCooperative Information Systems - 29th International Conference, CoopIS 2023, Proceedings
EditorsMohamed Sellami, Walid Gaaloul, Maria-Esther Vidal, Boudewijn van Dongen, Hervé Panetto
PublisherSpringer Science and Business Media Deutschland GmbH
Pages438-447
Number of pages10
ISBN (Print)9783031468452
DOIs
StatePublished - 2024
Event29th International Conference on Cooperative Information Systems, CoopIS 2023 - Groningen, Netherlands
Duration: 30 Oct 20233 Nov 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14353 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference29th International Conference on Cooperative Information Systems, CoopIS 2023
Country/TerritoryNetherlands
CityGroningen
Period30/10/233/11/23

Keywords

  • Annotation of Context Data
  • Process Logs
  • Process Model Enhancement
  • Process Models

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