Collaborative visual analysis with RCloud

Stephen North, Carlos Eduardo Scheidegger, Simon Urbanek, Gordon Woodhull

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Consider the emerging role of data science teams embedded in larger organizations. Individual analysts work on loosely related problems, and must share their findings with each other and the organization at large, moving results from exploratory data analyses (EDA) into automated visualizations, diagnostics and reports deployed for wider consumption. There are two problems with the current practice. First, there are gaps in this workflow: EDA is performed with one set of tools, and automated reports and deployments with another. Second, these environments often assume a single-developer perspective, while data scientist teams could get much benefit from easier sharing of scripts and data feeds, experiments, annotations, and automated recommendations, which are well beyond what traditional version control systems provide. We contribute and justify the following three requirements for systems built to support current data science teams and users: discoverability, technology transfer, and coexistence. In addition, we contribute the design and implementation of RCloud, a system that supports the requirements of collaborative data analysis, visualization and web deployment. About 100 people used RCloud for two years. We report on interviews with some of these users, and discuss design decisions, tradeoffs and limitations in comparison to other approaches.

Original languageEnglish (US)
Title of host publication2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-32
Number of pages8
ISBN (Electronic)9781467397834
DOIs
StatePublished - Dec 4 2015
Event10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Chicago, United States
Duration: Oct 25 2015Oct 30 2015

Other

Other10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015
CountryUnited States
CityChicago
Period10/25/1510/30/15

Keywords

  • collaboration
  • computer-supported cooperative work
  • provenance
  • visual analytics process
  • visualization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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    North, S., Scheidegger, C. E., Urbanek, S., & Woodhull, G. (2015). Collaborative visual analysis with RCloud. In 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings (pp. 25-32). [7347627] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VAST.2015.7347627