Towards Provenance Capturing of Quantified Self Data

Schreiber, Andreas; Seider, Doreen

doi:10.1007/978-3-319-40593-3_25

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…To make Quantified Self workflows provenance-aware requires to gather information that is required by the provenance model (see [7] for some possible approaches). This information is stored in a provenance database or provenance store.…”

Section: Provenance Of Electronic Datamentioning

confidence: 99%

“…• We shortly give an overview about provenance and our provenance model for Quantified Self data and self-tracking workflows [6,7] (Section 2).…”

Section: Introductionmentioning

confidence: 99%

“…Computers 2018, 7,12 Author Contributions: Andreas Schreiber conceived the concepts and experimental setup; Regina Struminksi implemented the automatic generation of provenance comics and performed the experiments; Andreas Schreiber wrote the paper. Regina Struminski contributed to the paper in Section 4 with paragraphs from her master thesis.…”

mentioning

confidence: 99%

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Visualizing the Provenance of Personal Data Using Comics

Schreiber

Struminski

2018

Computers

Self Cite

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Personal health data is acquired, processed, stored, and accessed using a variety of different devices, applications, and services. These are often complex and highly connected. Therefore, use or misuse of the data is hard to detect for people, if they are not capable to understand the trace (i.e., the provenance) of that data. We present a visualization technique for personal health data provenance using comic strips. Each strip of the comic represents a certain activity, such as entering data using a smartphone application, storing or retrieving data on a cloud service, or generating a diagram from the data. The comic strips are generated automatically using recorded provenance graphs. The easy-to-understand comics enable all people to notice crucial points regarding their data such as, for example, privacy violations.

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Section: Provenance Of Electronic Datamentioning

confidence: 99%

“…• We shortly give an overview about provenance and our provenance model for Quantified Self data and self-tracking workflows [6,7] (Section 2).…”

Section: Introductionmentioning

confidence: 99%