User Tasks

from the perspective of an Analyst

• High-Level Overview
  
  Show workflow runs, type and time
Attribute Encoding

Visual encoding of attributes (e.g., cell type, time, hierarchy level)
Filter

Trim irrelevant data
• Drill-Down on Demand
  
  Navigate among hierarchy levels
Investigate Changes

Communicate changes over time
Investigate Causality

Files and transformations that led to a particular result

Contributors

Stefan Luger	Holger Stitz	Samuel Gratzl	Nils Gehlenborg	Marc Streit

Bibliography

1. J. Kaiser, “The Cancer Test,” Science, vol. 348, no. 6242, pp. 1411–1413, 2015.
2. Y. L. Simmhan, B. Plale, and D. Gannon, “A Survey of Data Provenance in e-Science,” ACM SIGMOD Record, vol. 34, no. 3, pp. 31–36, 2005.
3. “Refinery Platform.” 2015.
4. L. Omberg, K. Ellrott, Y. Yuan, C. Kandoth, C. Wong, M. R. Kellen, S. H. Friend, J. Stuart, H. Liang, and A. A. Margolin, “Enabling Transparent and Collaborative Computational Analysis of 12 Tumor Types within The Cancer Genome Atlas,” Nat Genet, vol. 45, no. 10, pp. 1121–1126, 2013.
5. M. K. Anand, S. Bowers, and B. Ludaescher, “Provenance Browser: Displaying and Querying Scientific Workflow Provenance Graphs,” in Proceedings of the IEEE Conference on Data Engineering (ICDE ’10), 2010, pp. 1201–1204.
6. S. Davidson, S. Cohen-Boulakia, A. Eyal, B. Ludaescher, T. McPhillips, S. Bowers, and J. Freire, “Provenance in Scientific Workflow Systems,” IEEE Data Engineering Bulletin, vol. 30, no. 4, pp. 44–50, 2007.
7. M. I. Seltzer and P. Macko, “Provenance Map Orbiter: Interactive Exploration of Large Provenance Graphs,” in Proceedings of the USENIX Workshop on the Theory and Practice of Provenance (TaPP ’11), 2011.
8. F. Beck, M. Burch, S. Diehl, and D. Weiskopf, “The State of the Art in Visualizing Dynamic Graphs,” in Proceedings of the Eurographics Conference on Visualization (EuroVis ’14) -- State of The Art Reports, 2014.
9. N. Elmqvist and J.-D. Fekete, “Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines,” IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 3, pp. 439–454, 2010.
10. R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, “Network Motifs: Simple Building Blocks of Complex Networks,” Science, vol. 298, no. 5594, pp. 824–827, 2002.
11. E. Maguire, P. Rocca-Serra, S.-A. Sansone, J. Davies, and M. Chen, “Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs,” IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 2576–2585, 2013.
12. G. W. Furnas, “Generalized Fisheye Views,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’86), 1986, pp. 16–23.
13. F. van Ham and A. Perer, “‘Search, Show Context, Expand on Demand’: Supporting Large Graph Exploration with Degree-of-Interest,” IEEE Transactions on Visualization and Computer Graphics (InfoVis ’09), vol. 15, no. 6, pp. 953–960, 2009.
14. J. Abello, S. Hadlak, H. Schumann, and H.-J. Schulz, “A Modular Degree-of-Interest Specification for the Visual Analysis of Large Dynamic Networks,” IEEE Transactions on Visualization and Computer Graphics (InfoVis ’13), vol. 20, no. 3, pp. 337–350, 2014.

Bibliography cont'd.

1. S. Luger, H. Stitz, S. Gratzl, M. Streit, and N. Gehlenborg, “Interactive Visualization of Provenance Graphs for Reproducible Biomedical Research,” in Poster Proceedings of the Symposium on Biological Data Visualization (BioVis ’15), 2015.
2. S. Luger, H. Stitz, S. Gratzl, N. Gehlenborg, and M. Streit, “Interactive Visualization of Provenance Graphs for Reproducible Biomedical Research,” in Poster Compendium of the IEEE VIS Conference (InfoVis ’15), 2015.
3. H. Stitz, S. Gratzl, S. Luger, N. Gehlenborg, and M. Streit, “Transparent Layering for Visualizing Dynamic Graphs Using the Flip Book Metaphor,” in Poster Compendium of the IEEE VIS Conference (InfoVis ’14), 2014.
4. M. Streit and O. Bimber, “Visual Analytics: Seeking the Unknown,” Computer, vol. 46, no. 7, pp. 20–21, 2013.
5. T. Munzner, Visualization Analysis and Design. CRC Press, 2014.
6. N. Elmqvist, T.-N. Do, H. Goodell, N. Henry, and J. Fekete, “ZAME: Interactive Large-Scale Graph Visualization,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis ’08), 2008, pp. 215–222.
7. M. Ghoniem, J.-D. Fekete, and P. Castagliola, “A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations,” in Proceedings of the IEEE Symposium on Information Visualization (InfoVis ’04), 2004, pp. 17–24.
8. H.-J. Schulz and H. Schumann, “Visualizing Graphs - A Generalized View,” in Proceedings of the IEEE Conference on Information Visualisation (IV ’06), 2006, pp. 166–173.
9. B. Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” in Proceedings of the IEEE Symposium on Visual Languages (VL ’96), 1996, pp. 336–343.
10. B. Lee, C. Plaisant, C. S. Parr, J.-D. Fekete, and N. Henry, “Task Taxonomy for Graph Visualization,” in Proceedings of the AVI Workshop on Beyond time and errors: novel evaluation methods for information visualization (BELIV ’06), 2006, pp. 1–5.
11. “dagre - Graph Layout for JavaScript.” 2015.
12. K. Sugiyama, S. Tagawa, and M. Toda, “Methods for Visual Understanding of Hierarchical System Structures,” IEEE Transactions on Systems, Man and Cybernetics, vol. 11, no. 2, pp. 109–125, 1981.
13. E. Maguire, P. Rocca-Serra, S.-A. Sansone, J. Davies, and M. Chen, “Taxonomy-Based Glyph Design with a Case Study on Visualizing Workflows of Biological Experiments,” IEEE Transactions on Visualization and Computer Graphics (InfoVis ’12), vol. 18, no. 12, pp. 2603–2612, 2012.
14. D. Keim, J. Kohlhammer, G. Ellis, and F. Mansmann, Eds., Mastering the Information Age - Solving Problems with Visual Analytics. Eurographics Association, 2010.

Bibliography cont'd.

Please see thesis bibliography.

<thanks\>