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Towards Provenance Capturing of Quantified Self Data

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Provenance and Annotation of Data and Processes (IPAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9672))

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Abstract

Quantified Self or self-tracking is a growing movement where people are tracking data about themselves. Tracking the provenance of Quantified Self data is hard because usually many different devices, apps, and services are involved. Nevertheless receiving insights how the data has been acquired, how it has been processed, and who has stored and accessed it is crucial for people. We present concepts for tracking provenance in typical Quantified Self workflows. We use a provenance model based on PROV and show its feasibility with an example.

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Notes

  1. 1.

    http://play.google.com/store/apps/details?id=de.medando.weightcompanion.

  2. 2.

    http://lucmoreau.github.io/ProvToolbox/.

  3. 3.

    https://github.com/trungdong/prov.

References

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Author information

Authors and Affiliations

  1. German Aerospace Center (DLR), Cologne, Germany

    Andreas Schreiber

  2. Medando UG (haftungsbeschränkt), Cologne, Germany

    Doreen Seider

Authors
  1. Andreas Schreiber
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  2. Doreen Seider
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Corresponding author

Correspondence to Andreas Schreiber .

Editor information

Editors and Affiliations

  1. COPPE/UFRJ, Rio de Janeiro, Brazil

    Marta Mattoso

  2. Illinois Institute of Technology, Chicago, Illinois, USA

    Boris Glavic

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Schreiber, A., Seider, D. (2016). Towards Provenance Capturing of Quantified Self Data. In: Mattoso, M., Glavic, B. (eds) Provenance and Annotation of Data and Processes. IPAW 2016. Lecture Notes in Computer Science(), vol 9672. Springer, Cham. https://doi.org/10.1007/978-3-319-40593-3_25

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  • DOI: https://doi.org/10.1007/978-3-319-40593-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40592-6

  • Online ISBN: 978-3-319-40593-3

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