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Provenance framework for mHealth

Authors: Aarathi Prasad; Ronald A. Peterson; Shrirang Mare; Jacob Sorber; Kolin Paul; David Kotz;

Provenance framework for mHealth

Abstract

Mobile health technologies allow patients to collect their health information outside the hospital and share this information with others. But how can data consumers know whether to trust the sensor-collected and human-entered data they receive? Data consumers might be able to verify the accuracy and authenticity of the data if they have information about its origin and about changes made to it, i.e., the provenance of the data. We propose a provenance framework for mHealth devices, to collect and share provenance metadata and help the data consumer verify whether certain provenance properties are satisfied by the data they receive. This paper describes the programming model for this framework, which describes the rules to be implemented for providing provenance-collecting capabilities to an mHealth application.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Top 10%
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