Show simple item record Chen, Peng Plale, Beth Cheah, You-Wei Ghoshal, Devarshi Jensen, Scott Luo, Yuan 2012-09-28T17:44:00Z 2012-09-28T17:44:00Z 2012-09
dc.identifier.citation Peng Chen, Beth Plale, You-Wei Cheah, Devarshi Ghoshal, Scott Jensen, and Yuan Luo. “Visualization of Network Data Provenance.” Preprint of paper accepted for Workshop on Massive Data Analytics on Scalable Systems (DataMASS 2012), co-located with the IEEE International Conference on High Performance Computing (HiPC), submitted September 25, 2012. en_US
dc.description.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. en_US
dc.language.iso en en_US
dc.subject provenance visualization en_US
dc.subject graph matching en_US
dc.title Visualization of Network Data Provenance en_US
dc.type Preprint en_US
dc.altmetrics.display true en_US

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