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.
- Matplotlib documentation. http://matplotlib.org/.Google Scholar
- Spotfire recommendations. http://spotfire.tibco.com/recommendations.Google Scholar
- Vega-Lite documentation. https://vega.github.io/vega-lite/docs/.Google Scholar
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- M. Bostock, V. Ogievetsky, and J. Heer. D3 data-driven documents. IEEE Transactions on Visualization and Comp. Graphics, 17(12):2301--2309, 2011. Google ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- E. Horvitz. Principles of mixed-initiative user interfaces. In Proc. ACM Human Factors in Computing Systems (CHI), pages 159--166, 1999. Google ScholarDigital Library
- 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 ScholarDigital Library
- J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions on Graphics, 5(2):110--141, 1986. Google ScholarDigital Library
- 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 ScholarDigital Library
- D. B. Perry, B. Howe, A. M. Key, and C. Aragon. Vizdeck: Streamlining exploratory visual analytics of scientific data. 2013.Google Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer, 2009. Google ScholarDigital Library
- L. Wilkinson. The Grammar of Graphics. Springer, 2005. Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
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