Abstract
Information visualization construction tools generally tend to fall in one of two disparate categories. Either they offer simple but inflexible visualization templates, or else they offer low-level graphical primitives which need to be assembled manually. Those that do offer flexible, domain-specific abstractions rarely focus on incrementally building and transforming visualizations, which could reduce limitations on the style of workflows supported. We present a Haskell-embedded DSL for data visualization that is designed to provide such abstractions and transformations. This DSL achieves additional expressiveness and flexibility through common functional programming idioms and the Haskell type class hierarchy.
- R. Amar and J. Stasko. Knowledge precepts for design and evaluation of information visualizations. IEEE Trans. Visualization and Computer Graphics, 11(4):432–442, July 2005. Google Scholar
Digital Library
- R. Amar, J. Eagan, and J. Stasko. Low-level components of analytic activity in information visualization. In IEEE Symp. Information Visualization, pages 111–117, Oct 2005. Google Scholar
Digital Library
- J. Bertin. Graphics and graphic information processing. Morgan Kaufmann Publishers Inc., 1999. English translation.Google Scholar
- J. Bertin. Semiology of Graphics: Diagrams, Networks, Maps. University of Wisconsin Press, 2005. English translation.Google Scholar
- R. Borgo, D. Duke, M. Wallace, and C. Runciman. Multi-cultural visualization: how functional programming can enrich visualization (and vice versa). In Proc. Vision, Modeling, and Visualization, pages 245–252, 2006.Google Scholar
- M. Bostock and J. Heer. Protovis: A graphical toolkit for visualization. IEEE Trans. Visualization and Computer Graphics, 15(6):1121–1128, 2009. Google Scholar
Digital Library
- M. Bostock, V. Ogievetsky, and J. Heer. D3; data-driven documents. IEEE Trans. Visualization and Computer Graphics, 17(12):2301–2309, 2011. Google Scholar
Digital Library
- M. Brehmer and T. Munzner. A multi-level typology of abstract visualization tasks. IEEE Trans. Visualization and Computer Graphics, 19(12):2376–2385, Dec 2013. Google Scholar
Digital Library
- W. S. 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 Scholar
Cross Ref
- J. A. Cottam. Design and implementation of a stream-based visualization language. PhD thesis, Indiana University, 2011. Google Scholar
Digital Library
- D. J. Duke, R. Borgo, M. Wallace, and C. Runciman. Huge data but small programs: Visualization design via multiple embedded dsls. In Proc. Int. Symposium on Practical Aspects of Declarative Languages, pages 31–45, 2009. Google Scholar
Digital Library
- J. Fekete. The Infovis Toolkit. In IEEE Symp. Information Visualization, pages 167–174, 2004. 1 www.mathworks.com.au/products/matlab/ Google Scholar
Digital Library
- T. Green and M. Petre. Usability analysis of visual programming environments: A ’cognitive dimensions’ framework. Journal of Visual Languages & Computing, 7(2):131––174, 1996.Google Scholar
Cross Ref
- J. Heer and M. Bostock. Declarative language design for interactive visualization. IEEE Trans. Visualization and Computer Graphics, 16 (6):1149–1156, 2010. Google Scholar
Digital Library
- J. Heer, S. K. Card, and J. A. Landay. Prefuse: a toolkit for interactive information visualization. In ACM Proc. SIGCHI Conf. Human Factors in Computing Systems, pages 421–430, 2005. Google Scholar
Digital Library
- T. Herndon, M. Ash, and R. Pollin. Does high public debt consistently stifle economic growth? a critique of reinhart and rogoff. Cambridge Journal of Economics, 2013.Google Scholar
- D. Keim, F. Mansmann, J. Schneidewind, and H. Ziegler. Challenges in visual data analysis. In IEEE Int. Conf. Information Visualization, pages 9–16, July 2006. Google Scholar
Digital Library
- H. Kienle and H. Muller. Requirements of software visualization tools: A literature survey. In IEEE Work. Visualizing Software for Understanding and Analysis, pages 2–9, June 2007.Google Scholar
Cross Ref
- J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Trans. Graphics, 5(2):110–141, 1986. Google Scholar
Digital Library
- J. Mackinlay, P. Hanrahan, and C. Stolte. Show me: Automatic presentation for visual analysis. IEEE Trans. Visualization and Computer Graphics, 13(6):1137–1144, 2007. Google Scholar
Digital Library
- J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H. Byers. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, May 2011.Google Scholar
- K. Matlage and A. Gill. ChalkBoard: Mapping functions to polygons. In Proc. Symp. Implementation and Application of Functional Languages, volume 6041 of LNCS. Springer-Verlag, Sep 2009. Google Scholar
Digital Library
- R. Metoyer, B. Lee, N. Henry Riche, and M. Czerwinski. Understanding the verbal language and structure of end-user descriptions of data visualizations. In ACM Proc. SIGCHI Conf. Human Factors in Computing Systems, pages 1659–1662, 2012. Google Scholar
Digital Library
- C. Plaisant. The challenge of information visualization evaluation. In ACM Proc. Working Conference on Advanced Visual Interfaces, pages 109–116, 2004. Google Scholar
Digital Library
- K. Rogoff and C. Reinhart. Growth in a time of debt. American Economic Review, 100(2):573–578, 2010.Google Scholar
Cross Ref
- B. Shneiderman. The eyes have it: A task by data type taxonomy for information visualizations. In IEEE Symp. Visual Languages, pages 336–343, 1996. Google Scholar
Digital Library
- B. Shneiderman and C. Plaisant. Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies. In ACM Proc. AVI Workshop on Beyond Time and Errors: Novel Evaluation Methods for Information Visualization, pages 1–7, 2006. Google Scholar
Digital Library
- C. Stolte, D. Tang, and P. Hanrahan. Polaris: A system for query, analysis, and visualization of multidimensional relational databases. IEEE Trans. Visualization and Computer Graphics, 8(1):52–65, 2002. Google Scholar
Digital Library
- M. Theus. Interactive data visualization using Mondrian. Journal of Statistical Software, 7(11):1–9, 2003.Google Scholar
- J. van Wijk. The value of visualization. In IEEE Visualization, pages 79–86, Oct 2005.Google Scholar
- F. B. Viegas, M. Wattenberg, F. Van Ham, J. Kriss, and M. McKeon. Manyeyes: A site for visualization at internet scale. IEEE Trans. Visualization and Computer Graphics, 13(6):1121–1128, 2007. Google Scholar
Digital Library
- C. Weaver. Building highly-coordinated visualizations in improvise. In IEEE Symp. Information Visualization, pages 159–166, 2004. Google Scholar
Digital Library
- H. Wickham. ggplot2: Elegant graphics for data analysis. Springer-Verlag, 2nd edition, 2009. Google Scholar
Digital Library
- L. Wilkinson. The Grammar of Graphics. Springer-Verlag, 2nd edition, 2005. Google Scholar
Digital Library
- B. A. Yorgey. Monoids: theme and variations (functional pearl). In ACM Proc. Haskell symposium, pages 105–116, 2012. Introduction Visualization Components Creating Simple Charts Visualization Transformations Chart Composition and Layout Whitespace Visualization Functor Visualization Monad Visualization Comprehensions Evaluation Applicable Evaluation Schema Evaluation Conclusions Related Work Conclusion Google Scholar
Digital Library
Index Terms
A transformational approach to data visualization
Recommendations
A transformational approach to data visualization
GPCE 2014: Proceedings of the 2014 International Conference on Generative Programming: Concepts and ExperiencesInformation visualization construction tools generally tend to fall in one of two disparate categories. Either they offer simple but inflexible visualization templates, or else they offer low-level graphical primitives which need to be assembled ...
Extreme visualization: squeezing a billion records into a million pixels
SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of dataDatabase searches are usually performed with query languages and form fill in templates, with results displayed in tabular lists. However, excitement is building around dynamic queries sliders and other graphical selectors for query specification, with ...
Multivariate visualization with data fusion
We discuss a fusion-based visualization method to analyze a multivariate climate dataset and its metadata. The primary difference between a conventional visualization and a fusion-based visualization is that the former draws on a single image whereas ...






Comments