@inproceedings{
 title = {Visualizing large scale scientific data provenance},
 type = {inproceedings},
 year = {2012},
 id = {31116ee2-5468-33c7-b9ec-3e22b0d41fc3},
 created = {2019-10-01T17:20:45.146Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2019-10-01T17:23:17.452Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Chen2012c},
 folder_uuids = {73f994b4-a3be-4035-a6dd-3802077ce863},
 private_publication = {false},
 abstract = {Visualization increases the understanding of scientific data by facilitating exploration and explanation of the data. Provenance contributes to data understanding by exposing contributing factors that went in to producing a particular research result. However, provenance of scientific data can grow voluminous quickly because of the large amount of (intermediate) data and ever-increasing complexity. While previous research on visualizing provenance data focuses on small to medium sized provenance data, we develop visualization techniques for exploration and explanation of large scale provenance, including layout algorithm, visual style, graph abstraction techniques, graph matching algorithm, and temporal representation technique to deal with the high complexity. © 2012 IEEE.},
 bibtype = {inproceedings},
 author = {Chen, P. and Plale, B.},
 doi = {10.1109/SC.Companion.2012.205},
 booktitle = {Proceedings - 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012}
}

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