Provenance analysis for sensemaking. IEEE Computer Graphics and Applications, 39 (6) . pp. 27-29. ISSN 0272-1716

Fekete, Jean-Daniel, Jankun-Kelly, T. J., Tory, Melanie and Xu, Kai ORCID: https://orcid.org/0000-0003-2242-5440 (2019) Provenance analysis for sensemaking. IEEE Computer Graphics and Applications, 39 (6) . pp. 27-29. ISSN 0272-1716. [Journal Guest Editorial] (doi:10.1109/MCG.2019.2945378)

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Abstract

The articles in this special section examine the concept of "sensemaking", which refers to how we structure the unknown so as to be able to act in it. In the context of data analysis it involves understanding the data, generating hypotheses, selecting analysis methods, creating novel solutions, and critical thinking and learning wherever needed. Due to its explorative and creative nature, sensemaking is arguably the most challenging part of any data analysis.

Item Type: Journal Guest Editorial
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 31391
Notes on copyright: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Kai Xu
Date Deposited: 16 Nov 2020 09:28
Last Modified: 04 Feb 2021 20:58
URI: https://eprints.mdx.ac.uk/id/eprint/31391

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