VisTrails: enabling interactive multiple-view visualizations

By Louis Bavoil, Steven P. Callahan, Carlos Eduardo Scheidegger, H. Vo, Patricia J. Crossno, Cláudio T. Silva, Juliana Freire. VIS 05. IEEE Visualization, 2005., 2005

437 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/bcc3c02af8efae5a710f4bb53dbdd6067237773a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Samuel Madden
2. Amol Deshpande
3. Juliana Freire
4. Amit Chavan
5. Hui Miao
6. Aditya G. Parameswaran
7. Eugene Wu
8. M. Franklin

In Computer Vision and Pattern Recognition

1. Juliana Freire
2. Samuel Madden
3. Aditya G. Parameswaran
4. Michelle X. Zhou
5. David Gotz
6. Carlos Eduardo Scheidegger
7. Cláudio T. Silva
8. Eugene Wu

In Information Systems and Management

1. Amol Deshpande
2. Amit Chavan
3. Hui Miao
4. Samuel Madden
5. Eugene Wu
6. R. Hull

Citations in the corpus

• Managing Rapidly-Evolving Scientific Workflows (2006, background): “For more details, the reader is referred to [3,5].”
• DataHub: Collaborative Data Science & Dataset Version Management at Scale (2014, background): “id. Mostdatasciencesystems,includinganalyticpackageslikeSAS, Excel, R, Matlab, Mahout, Scikit ( scikit-learn.org ), and Pandas (pandas.pydata.org );workowtoolslikePegasus[4],Chimera[6], and VisTrails [1]; and collaboration tools like Fusion tables [12], Orchestra [8], and CQMS [10, 7] lack dataset v”
• VisTrails: visualization meets data management (2006, methodology): “We only sketch the main features of the system here, for further details see [2, 3].”
• Characterizing Users' Visual Analytic Activity for Insight Provenance (2008, background): “For example, the VisTrails system [3] captures chains of visualization operations that represent scientific visualization workflows.”
• Combining Design and Performance in a Data Visualization Management System (2017, background): “Provenance Visualization: the problem of provenance visualization [9, 26, 41] is very challenging; partly because it quickly de-volves into a network visualization problem.”
• Provenance Collection Support in the Kepler Scientific Workflow System (2006, methodology): “The VisTrails system [15] was developed to facilitate interactive multiple-view visualizations by providing a general infrastructure, which can be used in conjunction with any existing visualization system, like Kitware's Visualization Toolkit [17], to create and maintain visualization workflows as ”
• ProvDB: A System for Lifecycle Management of Collaborative Analysis Workflows (2016, background): “In scientific workflow systems [1,2,12,13,13,14,29,33,35,36,54] where the operations are typically treated as black boxes, the provenance can usually be captured only at the level of datasets.”