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Cited by 42Data Visualization and AnalyticsScientific Computing and Data ManagementComputer Graphics and Visualization Techniques

Visualization of network data provenance

Chen, Peng, Plale, Beth, Cheah, You-Wei, Ghoshal, Devarshi, Jensen, Scott, Luo, Yuan·2012·2012 19th International Conference on High Performance Computing
DOI 10.1109/hipc.2012.6507517
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Abstract

Visualization facilitates the understanding of scientific data both through exploration and explanation of the visualized data. Provenance also contributes to the understanding of data by containing the contributing factors behind a result. The visualization of provenance, although supported in existing workflow management systems, generally focuses on small (medium) sized provenance data, lacking techniques to deal with big data with high complexity. This paper discusses visualization techniques developed for exploration and explanation of provenance, including layout algorithm, visual style, graph abstraction techniques, and graph matching algorithm, to deal with the high complexity. We demonstrate through application to two extensively analyzed case studies that involved provenance capture and use over three year projects, the first involving provenance of a satellite imagery ingest processing pipeline and the other of provenance in a large-scale computer network testbed.

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21 of 21 references verified0 citation errors · 0 not checkable

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  • The general opacity of interconnections between systems, coupled with the general lack of technical means for tracking data flow at such a scale, poses accountability issues.

    “The general opacity of the interconnections between systems, coupled with the general lack of technical means for tracking data flow at such a scale, therefore poses accountability issues, as it becomes difficult to discern the technical components involved, and then who is responsible for these [22], [23].”

    From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [23]

  • More information regarding the nature of systems could allow those building and deploying systems to improve their quality and identify points of failure, while empowering others by offering more insight into systems and data beyond their direct control.

    “Generally speaking, more information regarding the nature of systems could allow those building and deploying systems to improve their quality and to identify points of failure, while empowering others by offering more insight into the nature of the systems and data that is beyond their direct control [4], [22], [23].”

    From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [23]

  • Although provenance has been considered for some specific compliance aspects, the broader accountability considerations have yet to be explored in depth.

    “Though, as mentioned, provenance has been considered for some specific compliance aspects (see for e.g.[22], [23], [27]- [29]), the broader accountability considerations have yet to be explored in depth.”

    From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [23]

  • Servicing many data protection rights could be facilitated through mechanisms for tracing the paths of data to identify where data resides, with whom it was shared, and how it was processed.

    “As discussed in §II, servicing many of the rights afforded to individuals in data protection law could be facilitated through mechanisms for tracing the paths of data so as to identify where data resides, with whom it was shared, and how it was processed (e.g.see [23], [28]).”

    From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [23]

  • The security of and access to provenance data raises accountability questions, given that it may itself be sensitive or come with various legal obligations, particularly in a cross-organisational context.

    “Further, the security of, and access to the provenance data itself raises accountability questions, given that, as information about data, systems and processing, it may itself be sensitive [23], or come with various legal obligations -particularly in a cross-organisational context.”

    From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [23]

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Authors
Chen, P., Plale, B., Cheah, Y.-W., Ghoshal, D., Jensen, S., & Luo, Y.
Affiliations
Indiana University Bloomington (US)
ORCID
Venue
2012 19th International Conference on High Performance Computing
Publisher
IEEE
Published
2012-12
Pages
1-9
DOI
10.1109/hipc.2012.6507517
OpenAlex
W1977690765
Cited by
42
References
21
Citations by year
2026: 1 · 2025: 1 · 2024: 2 · 2023: 3 · 2022: 1 · 2021: 4 · 2019: 2 · 2018: 7 · 2017: 2 · 2016: 3 · 2015: 8 · 2014: 1 · 2013: 6 · 2012: 1
FWCI
3.63
Type
conference paper
Language
en
Open access
closed
Licence
Locations
  • IEEE International Conference on High Performance Computing, Data, and Analytics (publishedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion)
Funders
Clemson University
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
  • Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
Keywords
provenance visualization, graph layout algorithms, visual style, graph abstraction, graph matching, network testbed

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