Self-tracking reloaded : applying process mining to personalized health care from labeled sensor data


Sztyler, Timo ; Carmona, Josep ; Völker, Johanna ; Stuckenschmidt, Heiner



DOI: https://doi.org/10.1007/978-3-662-53401-4_8
URL: http://link.springer.com/chapter/10.1007%2F978-3-6...
Additional URL: http://publications.wim.uni-mannheim.de/informatik...
Document Type: Book chapter
Year of publication: 2016
Book title: Transactions on Petri nets and other models of concurrency XI
The title of a journal, publication series: Lecture Notes in Computer Science
Volume: 9930
Page range: 160-180
Publisher: Koutny, Maciej
Place of publication: Berlin ; Heidelberg ; New York, NY
Publishing house: Springer
ISBN: 978-3-662-53400-7 , 978-3-662-53401-4
ISSN: 1867-7193 , 1867-7746
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: Currently, there is a trend to promote personalized health care in order to prevent diseases or to have a healthier life. Using current devices such as smart-phones and smart-watches, an individual can easily record detailed data from her daily life. Yet, this data has been mainly used for {\em self-tracking} in order to enable personalized health care. In this paper, we provide ideas on how process mining can be used as a fine-grained evolution of traditional self-tracking. We have applied the ideas of the paper on recorded data from a set of individuals, and present conclusions and challenges.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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