Towards provenance capturing of quantified self data

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.