skip to main content
10.1145/2939502.2939506acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
research-article

Towards a general-purpose query language for visualization recommendation

Published:26 June 2016Publication History

ABSTRACT

Creating effective visualizations requires domain familiarity as well as design and analysis expertise, and may impose a tedious specification process. To address these difficulties, many visualization tools complement manual specification with recommendations. However, designing interfaces, ranking metrics, and scalable recommender systems remain important research challenges. In this paper, we propose a common framework for facilitating the development of visualization recommender systems in the form of a specification language for querying over the space of visualizations. We present the preliminary design of CompassQL, which defines (1) a partial specification that describes enumeration constraints, and (2) methods for choosing, ranking, and grouping recommended visualizations. To demonstrate the expressivity of the language, we describe existing recommender systems in terms of CompassQL queries. Finally, we discuss the prospective benefits of a common language for future visualization recommender systems.

References

  1. Matplotlib documentation. http://matplotlib.org/.Google ScholarGoogle Scholar
  2. Spotfire recommendations. http://spotfire.tibco.com/recommendations.Google ScholarGoogle Scholar
  3. Vega-Lite documentation. https://vega.github.io/vega-lite/docs/.Google ScholarGoogle Scholar
  4. A. Anand and J. Talbot. Automatic selection of partitioning variables for small multiple displays. Visualization and Computer Graphics, IEEE Transactions on, 22(1):669--677, 2016.Google ScholarGoogle Scholar
  5. R. A. Becker, W. S. Cleveland, and M.-J. Shyu. The visual design and control of trellis display. Journal of computational and Graphical Statistics, 5(2):123--155, 1996.Google ScholarGoogle Scholar
  6. E. Bertini, A. Tatu, and D. Keim. Quality metrics in high-dimensional data visualization: an overview and systematization. IEEE Transactions on Visualization and Comp. Graphics, 17(12):2203--2212, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Bostock, V. Ogievetsky, and J. Heer. D3 data-driven documents. IEEE Transactions on Visualization and Comp. Graphics, 17(12):2301--2309, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. W. Cleveland and R. McGill. Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387):531--554, 1984.Google ScholarGoogle ScholarCross RefCross Ref
  9. L. Grammel, M. Tory, and M. Storey. How information visualization novices construct visualizations. IEEE Transactions on Visualization and Comp. Graphics, 16(6):943--952, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. E. Horvitz. Principles of mixed-initiative user interfaces. In Proc. ACM Human Factors in Computing Systems (CHI), pages 159--166, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. S. Kandel, R. Parikh, A. Paepcke, J. M. Hellerstein, and J. Heer. Profiler: Integrated statistical analysis and visualization for data quality assessment. In Proc. Advanced Visual Interfaces (AVI), pages 547--554. ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions on Graphics, 5(2):110--141, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. J. Mackinlay, P. Hanrahan, and C. Stolte. Show me: Automatic presentation for visual analysis. IEEE Transactions on Visualization and Comp. Graphics, 13(6):1137--1144, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. D. B. Perry, B. Howe, A. M. Key, and C. Aragon. Vizdeck: Streamlining exploratory visual analytics of scientific data. 2013.Google ScholarGoogle Scholar
  15. A. Satyanarayan, R. Russell, J. Hoffswell, and J. Heer. Reactive vega: A streaming dataflow architecture for declarative interactive visualization. Visualization and Computer Graphics, IEEE Transactions on, 22(1):659--668, 2016.Google ScholarGoogle Scholar
  16. J. Seo and B. Shneiderman. A rank-by-feature framework for interactive exploration of multidimensional data. IEEE Transactions on Visualization and Comp. Graphics, 4(2):96--113, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. C. Stolte, D. Tang, and P. Hanrahan. Polaris: A System for Query, Analysis, and Visualization of Multidimensional Relational Databases. IEEE Transactions on Visualization and Comp. Graphics, 8(1):52--65, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. W. Tukey and P. A. Tukey. Computer graphics and explaoratory data analysis: An introduction. In Proceedings of the Sixth Annual Conference and Exposition: Computer Graphics, 1985.Google ScholarGoogle Scholar
  19. S. van den Elzen and J. J. van Wijk. Small multiples, large singles: A new approach for visual data exploration. Computer Graphics Forum, 32(3pt2):191--200, 2013.Google ScholarGoogle Scholar
  20. M. Vartak, S. Huang, T. Siddiqui, S. Madden, and A. Parameswaran. Towards visualization recommendation systems. Workshop on Data Systems for Interactive Analytics (DSIA), 2015.Google ScholarGoogle Scholar
  21. M. Vartak, S. Rahman, S. Madden, A. Parameswaran, and N. Polyzotis. SeeDB: Efficient data-driven visualization recommendations to support visual analytics. VLDB 2015, 8(13):2182--2193, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. L. Wilkinson. The Grammar of Graphics. Springer, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. L. Wilkinson, A. Anand, and R. L. Grossman. Graph-theoretic scagnostics. In IEEE Transactions on Visualization and Comp. Graphics, volume 5, page 21, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. K. Wongsuphasawat, D. Moritz, A. Anand, J. Mackinlay, B. Howe, and J. Heer. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. IEEE Transactions on Visualization and Comp. Graphics, 22(1):649--658, 2016.Google ScholarGoogle ScholarCross RefCross Ref

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    HILDA '16: Proceedings of the Workshop on Human-In-the-Loop Data Analytics
    June 2016
    93 pages
    ISBN:9781450342070
    DOI:10.1145/2939502

    Copyright © 2016 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 26 June 2016

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article

    Acceptance Rates

    HILDA '16 Paper Acceptance Rate16of32submissions,50%Overall Acceptance Rate28of56submissions,50%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader