Quantified Self became popular in recent years. People are tracking themselves with Wearables, smartphone apps, or desktop applications. They collect, process and store huge amounts of personal data for medical and other reasons. Due to the complexity of different data sources, apps, and cloud services, it is hard to follow the data flow and to have trust in data integrity and safety. We present a solution that helps to get insight in Quantified Self data flows and to answer questions related to data security. We provide a provenance model for Quantified Self data based on the W3C standard PROV. Using that model, developers and users can record provenance of Quantified Self apps and services with a standardized notation. We show the feasibility of the presented provenance model with a small workflow using steps data from Fitbit fitness tracker.


Provenance Quantified self Personal informatics Trust Ontology PROV 



We would like to thank Bojan Janisch (Bonn-Rhein-Sieg University of Applied Sciences), for his supportive work he did during his master project.


  1. 1.
    Bavoil, L., Callahan, S.P., Crossno, P.J., Freire, J., Vo, H.T.: VisTrails: enabling interactive multiple-view visualizations. In: pp. 135–142. IEEE (2005)Google Scholar
  2. 2.
    Choe, E.K., Lee, N.B., Lee, B., Pratt, W., Kientz, J.A.: Understanding quantified-selfers’ practices in collecting and exploring personal data. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pp. 1143–1152. ACM (2014)Google Scholar
  3. 3.
    Fitbit: Fitbit developer api (2016).
  4. 4.
    Hoekstra, R., Groth, P.: PROV-O-Viz - understanding the role of activities in provenance. In: Ludäescher, B., Plale, B. (eds.) IPAW 2014. LNCS, vol. 8628, pp. 215–220. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  5. 5.
    Hoy, M.B.: Personal activity trackers and the quantified self. Med. Ref. Serv. Q. 35(1), 94–100 (2016)CrossRefGoogle Scholar
  6. 6.
    Hunter, J.D.: Matplotlib: a 2d graphics environment. Comput. Sci. Eng. 9(3), 90–95 (2007)CrossRefGoogle Scholar
  7. 7.
    Huynh, T.D.: A python library for W3C provenance data model supporting PROV-JSON import/export (2014).
  8. 8.
    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
  9. 9.
    Janisch, B.: Developing an abstract Quantified Self Provenance-model. Master project, University of Applied Sciences Bonn-Rhein-Sieg (2015).
  10. 10.
    Jones, S.L.: Exploring correlational information in aggregated quantified self data dashboards. In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 1075–1080. ACM (2015)Google Scholar
  11. 11.
    Kelly, I.: python-fitbit - fitbit api python client implementation (2016).
  12. 12.
    Lebo, T., Sahoo, S., McGuinness, D., Belhajjame, K., Cheney, J., Corsar, D., Garijo, D., Soiland-Reyes, S., Zednik, S., Zhao, J.: PROV-O: The PROV ontology, 30 April 2013.
  13. 13.
    Mckinney, W.: pandas: a foundational python library for data analysis and statistics. In: PyHPC 2011: Workshop on Python for High Performance and Scientific Computing, SC11, Seattle, WA, USA, 18 November 2011Google Scholar
  14. 14.
    Moreau, L., Groth, P., Cheney, J., Lebo, T., Miles, S.: The rationale of PROV. Web Seman. Sci. Serv. Agents World Wide Web 35, Part 4, 235–257 (2015)Google Scholar
  15. 15.
    Moreau, L., Groth, P., Miles, S., Vazquez-Salceda, J., Ibbotson, J., Jiang, S., Munroe, S., Rana, O., Schreiber, A., Tan, V., Varga, L.: The provenance of electronic data. Commun. ACM 51(4), 52–58 (2008)CrossRefGoogle Scholar
  16. 16.
    Moreau, L., Missier, P., Belhajjame, K., B’Far, R., Cheney, J., Coppens, S., Cresswell, S., Gil, Y., Groth, P., Klyne, G., Lebo, T., McCusker, J., Miles, S., Myers, J., Sahoo, S., Tilmes, C.: PROV-DM: The PROV data model, 30 April 2013.
  17. 17.
    Noy, N.F., Mcguinness, D.L.: Ontology development 101: A guide to creating your first ontology (2001).
  18. 18.
    Picard, R., Wolf, G.: Sensor informatics and quantified self. IEEE J. Biomed. Health Inform. 19(5), 1531 (2015)CrossRefGoogle Scholar
  19. 19.
    QSEU14: Breakout: Mapping data access, 23 August 2014.
  20. 20.
    Schreiber, A., Ney, M., Wendel, H.: The provenance store prOOst for the open provenance model. In: Groth, P., Frew, J. (eds.) IPAW 2012. LNCS, vol. 7525, pp. 240–242. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Distributed Systems and Component SoftwareGerman Aerospace Center (DLR)CologneGermany

Personalised recommendations