Literature DB >> 26340779

Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes.

Eric D Ragan, Alex Endert, Jibonananda Sanyal, Jian Chen.   

Abstract

While the primary goal of visual analytics research is to improve the quality of insights and findings, a substantial amount of research in provenance has focused on the history of changes and advances throughout the analysis process. The term, provenance, has been used in a variety of ways to describe different types of records and histories related to visualization. The existing body of provenance research has grown to a point where the consolidation of design knowledge requires cross-referencing a variety of projects and studies spanning multiple domain areas. We present an organizational framework of the different types of provenance information and purposes for why they are desired in the field of visual analytics. Our organization is intended to serve as a framework to help researchers specify types of provenance and coordinate design knowledge across projects. We also discuss the relationships between these factors and the methods used to capture provenance information. In addition, our organization can be used to guide the selection of evaluation methodology and the comparison of study outcomes in provenance research.

Year:  2015        PMID: 26340779     DOI: 10.1109/TVCG.2015.2467551

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  5 in total

1.  Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data.

Authors:  Robert Krueger; Johanna Beyer; Won-Dong Jang; Nam Wook Kim; Artem Sokolov; Peter K Sorger; Hanspeter Pfister
Journal:  IEEE Trans Vis Comput Graph       Date:  2019-09-10       Impact factor: 4.579

2.  From Visual Exploration to Storytelling and Back Again.

Authors:  S Gratzl; A Lex; N Gehlenborg; N Cosgrove; M Streit
Journal:  Comput Graph Forum       Date:  2016-07-04       Impact factor: 2.078

3.  ENIGMA-Viewer: interactive visualization strategies for conveying effect sizes in meta-analysis.

Authors:  Guohao Zhang; Peter Kochunov; Elliot Hong; Sinead Kelly; Christopher Whelan; Neda Jahanshad; Paul Thompson; Jian Chen
Journal:  BMC Bioinformatics       Date:  2017-06-06       Impact factor: 3.169

4.  AVOCADO: Visualization of Workflow-Derived Data Provenance for Reproducible Biomedical Research.

Authors:  H Stitz; S Luger; M Streit; N Gehlenborg
Journal:  Comput Graph Forum       Date:  2016-07-04       Impact factor: 2.078

5.  Data Integration for Future Medicine (DIFUTURE).

Authors:  Fabian Prasser; Oliver Kohlbacher; Ulrich Mansmann; Bernhard Bauer; Klaus A Kuhn
Journal:  Methods Inf Med       Date:  2018-07-17       Impact factor: 2.176

  5 in total

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