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Zeitschriftenartikel:

C. Bors, J. Wenskovitch, M. Dowling, S. Attfield, L. Battle, A. Endert, O. Kulyk, R. Laramee:
"A Provenance Task Abstraction Framework";
IEEE Computer Graphics and Applications, 39 (2019), 6; S. 46 - 60.



Kurzfassung englisch:
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.

Schlagworte:
Task analysis, Data visualization, Visualization, Cognition, Analytical models, History, Provenance, Task Abstraction, Provenance Hierarchy, Visual Analytics, Framework, Conceptual Model, Sensemaking


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/MCG.2019.2945720

Elektronische Version der Publikation:
https://publik.tuwien.ac.at/files/publik_282245.pdf



Zugeordnete Projekte:
Projektleitung Silvia Miksch:
Visuelle Segmentierung und Labeling multivariater Zeitserien


Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.