A visualization architecture for collaborative analytical and data provenance activities

A Al-Naser, M Rasheed, D Irving, J M Brooke

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

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Abstract

When exploring noisy or visually complex data, such as seismic data from the oil and gas industry, it is often the case that algorithms cannot completely identify features of interest. Human intuition must complete the process. Given the nature of intuition, this can be a source of differing interpretations depending on the human expert, thus we do not have a single feature but multiple views of a feature. Managing multi-user and multi-version interpretations, combined with version tracking, is challenging as these interpretations are often stored as geometric objects separately from the raw data and possibly in different local machines. In this paper we combine the storage of the raw data with the storage of the interpretations produced by the visualization of features by multiple user sessions. We present case studies that illustrate our system's ability to reproduce users' amendments to the interpretations of others and the ability to retrace the history of amendments to a visual feature. © 2013 IEEE.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Information Visualisation|Proc. Int. Conf. Inf. Visual.
PublisherIEEE
Pages253-262
Number of pages9
ISBN (Print)9780769550497
DOIs
Publication statusPublished - 2013
Event2013 17th International Conference on Information Visualisation, IV 2013 - London
Duration: 1 Jul 2013 → …
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6676571

Conference

Conference2013 17th International Conference on Information Visualisation, IV 2013
CityLondon
Period1/07/13 → …
Internet address

Keywords

  • Data acquisition and management
  • Data exploration
  • Geospatial visualization
  • Provenance
  • Query-driven visualization

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