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Visual Analytics: Data, Analytical and Reasoning Provenance

In: Building Trust in Information

Author

Listed:
  • Margaret Varga

    (Seetru Ltd
    University of Oxford)

  • Caroline Varga

    (Seetru Ltd)

Abstract

Analysts and decision makers are increasingly overloaded with vast amounts of data/information which are often dynamic, complex, disparate, conflicting, incomplete and, at times, uncertain. Furthermore, problems and tasks that require their attention can be ambiguous, i.e. they are ill-defined. In order to make sense of complex data and situations and make informed decisions, they utilize their intuition, knowledge and experience. Provenance is fundamental for the user to capture and exploit effectively the explicit data and implicit knowledge within the decision making process. Provenance can usefully be considered at three conceptual levels, namely: data (what), analytical (how) and reasoning (why). This paper explores visual analytics in the exploitation of provenance within the decision making process.

Suggested Citation

  • Margaret Varga & Caroline Varga, 2016. "Visual Analytics: Data, Analytical and Reasoning Provenance," Springer Proceedings in Business and Economics, in: Victoria L. Lemieux (ed.), Building Trust in Information, pages 141-150, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-40226-0_9
    DOI: 10.1007/978-3-319-40226-0_9
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    Cited by:

    1. Sheikh S. Abdullah & Neda Rostamzadeh & Kamran Sedig & Amit X. Garg & Eric McArthur, 2020. "Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3," Data, MDPI, vol. 5(2), pages 1-24, March.

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