A Provenance Task Abstraction Framework. Bors, C., Wenskovitch, J., Dowling, M., Attfield, S., Battle, L., Endert, A., Kulyk, O., & Laramee, R. S. IEEE Computer Graphics and Applications, 39(6):46–60, November, 2019. doi abstract bibtex Visual analytics tools integrate provenance recording to externalize analytic processes or user insights. Provenance can be captured on varying levels of detail, and in turn activities can be characterized from different granularities. However, current approaches do not support inferring activities that can only be characterized across multiple levels of provenance. We propose a task abstraction framework that consists of a three stage approach, composed of 1) initializing a provenance task hierarchy, 2) parsing the provenance hierarchy by using an abstraction mapping mechanism, and 3) leveraging the task hierarchy in an analytical tool. Furthermore, we identify implications to accommodate iterative refinement, context, variability, and uncertainty during all stages of the framework. We describe a use case which exemplifies our abstraction framework, demonstrating how context can influence the provenance hierarchy to support analysis. The article concludes with an agenda, raising and discussing challenges that need to be considered for successfully implementing such a framework.
@article{bors_provenance_2019,
title = {A {Provenance} {Task} {Abstraction} {Framework}},
volume = {39},
issn = {1558-1756},
doi = {10.1109/MCG.2019.2945720},
abstract = {Visual analytics tools integrate provenance recording to externalize analytic processes or user insights. Provenance can be captured on varying levels of detail, and in turn activities can be characterized from different granularities. However, current approaches do not support inferring activities that can only be characterized across multiple levels of provenance. We propose a task abstraction framework that consists of a three stage approach, composed of 1) initializing a provenance task hierarchy, 2) parsing the provenance hierarchy by using an abstraction mapping mechanism, and 3) leveraging the task hierarchy in an analytical tool. Furthermore, we identify implications to accommodate iterative refinement, context, variability, and uncertainty during all stages of the framework. We describe a use case which exemplifies our abstraction framework, demonstrating how context can influence the provenance hierarchy to support analysis. The article concludes with an agenda, raising and discussing challenges that need to be considered for successfully implementing such a framework.},
number = {6},
journal = {IEEE Computer Graphics and Applications},
author = {Bors, Christian and Wenskovitch, John and Dowling, Michelle and Attfield, Simon and Battle, Leilani and Endert, Alex and Kulyk, Olga and Laramee, Robert S.},
month = nov,
year = {2019},
keywords = {WHY - User Behaviour, User Characteristics, User Modelling, HOW - Classification Models, WHEN - Retrospective Analyses, Type of Work: Theory \& Model},
pages = {46--60},
file = {IEEE Xplore Full Text PDF:C\:\\Users\\conny\\Zotero\\storage\\4MSRJCZ5\\Bors et al. - 2019 - A Provenance Task Abstraction Framework.pdf:application/pdf}
}
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