Advertisement

Towards Provenance Capturing of Quantified Self Data

  • Andreas SchreiberEmail author
  • Doreen Seider
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9672)

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.

Keywords

Provenance Quantified self Wearables PROV 

References

  1. 1.
    Allen, M.D., Chapman, A., Blaustein, B., Seligman, L.: Capturing provenance in the wild. In: McGuinness, D.L., Michaelis, J.R., Moreau, L. (eds.) IPAW 2010. LNCS, vol. 6378, pp. 98–101. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Bachmann, A., Bergmeyer, H., Schreiber, A.: Evaluation of aspect-oriented frameworks in python for extending a project with provenance documentation features. Python Pap. 6(3), 3 (2011)Google Scholar
  3. 3.
    Hoy, M.B.: Personal activity trackers and the quantified self. Med. Ref. Serv. Q 35(1), 94–100 (2016)CrossRefGoogle Scholar
  4. 4.
    Huynh, T.D., Moreau, L.: ProvStore: a public provenance repository. In: Ludaescher, B., Plale, B. (eds.) IPAW 2014. LNCS, vol. 8628, pp. 275–277. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  5. 5.
    Janisch, B.: Developing an abstract Quantified Self Provenance model. Master project, University of Applied Sciences Bonn-Rhein-Sieg (2015). http://elib.dlr.de/100752/
  6. 6.
    McPhillips, T., et al.: Yesworkflow: a user-oriented, language-independent tool for recovering workflow information from scripts. Int. J. Digit. Curation 10(1), 298–313 (2015)CrossRefGoogle Scholar
  7. 7.
    Murta, L., Braganholo, V., Chirigati, F., Koop, D., Freire, J.: noWorkflow: capturing and analyzing provenance of scripts. In: Ludaescher, B., Plale, B. (eds.) IPAW 2014. LNCS, vol. 8628, pp. 71–83. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  8. 8.
    Picard, R., Wolf, G.: Sensor informatics and quantified self. IEEE J. Biomed. Health Inf. 19(5), 1531 (2015)CrossRefGoogle Scholar
  9. 9.
    Schreiber, A.: A provenance model for quantified self data. In: Antona, M., Stephanidis, C. (eds.) UAHCI 2016, Part I. LNCS, vol. 9737. Springer, Switzerland (2016)Google Scholar
  10. 10.
    Stamatogiannakis, M., Groth, P., Bos, H.: Looking inside the black-box: capturing data provenance using dynamic instrumentation. In: Ludaescher, B., Plale, B. (eds.) IPAW 2014. LNCS, vol. 8628, pp. 155–167. Springer, Heidelberg (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.German Aerospace Center (DLR)CologneGermany
  2. 2.Medando UG (haftungsbeschränkt)CologneGermany

Personalised recommendations