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 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.
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Notes
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So far, we do not consider the sub-activities in the presented use cases.
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Sequence mining techniques may in principle extract similar patterns. One difference is the inability for these techniques to present process view of the extracted patterns.
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Acknowledgments
This work as been partially supported by funds from the Ministry for Economy and Competitiveness (MINECO) of Spain and the European Union (FEDER funds) under grant COMMAS (ref. TIN2013-46181-C2-1-R).
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Sztyler, T., Carmona, J., Völker, J., Stuckenschmidt, H. (2016). Self-tracking Reloaded: Applying Process Mining to Personalized Health Care from Labeled Sensor Data. In: Koutny, M., Desel, J., Kleijn, J. (eds) Transactions on Petri Nets and Other Models of Concurrency XI. Lecture Notes in Computer Science(), vol 9930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53401-4_8
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