Abstract
This chapter focuses on the visualization of multi-domain search results. We start by positioning the problem in the recent line of evolution of search engine interfaces, which more and more are capable of mining semantic concepts and associations from text data and presenting them in sophisticated ways that depend on the type of the extracted data. The approach to visualization proposed in search computing extends current practices in several ways: the data to visualize are N-dimensional combinations of objects, with ranking criteria associated both to individual objects and to sets of combinations; object’s properties can be classified in several types, for which optimized visualization families are preferred (e.g., timelines for temporal data, maps for geo-located information); combinations may exhibit any number of relevant properties to be displayed, which need to fit to the bi-dimensional presentation space, by emphasizing the most important attributes and de-emphasizing or hiding the less important ones. The visualization problem therefore amounts to deciding the best mapping between the data of the result set and the visualization space.
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References
Bertin, J.: Semiology of Graphics: Diagrams, Networks, Maps (1983)
Bostock, M., Heer, J.: Protovis: A Graphical Toolkit for Visualization. IEEE Trans. Vis. Comput. Graph. 15(6), 1121–1128 (2009)
Bozzon, A., Brambilla, M., Ceri, S., Fraternali, P., Manolescu, I.: Liquid Queries and Liquid Results in Search Computing. In: Ceri, S., Brambilla, M. (eds.) Search Computing. LNCS, vol. 5950, pp. 244–267. Springer, Heidelberg (2010)
Catarci, T., Dix, A.J., Kimani, S., Santucci, G.: User-Centered Data Management. Morgan & Claypool Publishers, San Francisco (2010)
Catarci, T., Santucci, G., Costabile, M.F., Cruz, I.: Foundations of the DARE System for Drawing Adequate Representations. In: Proc. of DANTE 1999 (1999)
Chi, E.H.: A Taxonomy of Visualization Techniques Using the Data State Reference Model. In: IEEE Symp. Information Visualization, Salt Lake City, UT, USA, pp. 69–75. IEEE CS Press, Los Alamitos (2000)
Daassi, C., Nigay, L., Fauvet, M.C.: A Taxonomy of Temporal Data Visualization Techniques. Information-Interaction-Intelligence 5(2), 41–63 (2005)
Elmqvist, N., Stasko, J., Tsigas, P., Meadow, D.: A Visual Canvas for Analysis of Large-Scale Multivariate Data. Information Visualization 7(1), 18–33 (2008)
Fernandes Silva, S., Catarci, T.: Visualization of Linear Time-Oriented Data: A Survey. Journal of Applied System Studies 3(2) (2002)
Fox, E.A., Hix, D., Nowell, L.T., Brueni, D.J., Wake, W.C., Heath, L.S., Rao, D.: Users, User Interfaces, and Objects: Envision, a Digital Library. Journal of the American Society for Information Science 44(8), 480–491 (1993)
Friendly, M.: Visualizing Categorical Data. SAS Publishing (2000)
Garlandini, S., Fabrikant, S.I.: Evaluating the Effectiveness and Efficiency of Visual Variables for Geographic Information Visualization. In: Hornsby, K.S., Claramunt, C., Denis, M., Ligozat, G. (eds.) COSIT 2009. LNCS, vol. 5756, pp. 195–211. Springer, Heidelberg (2009)
GeoNames Ontology, http://www.geonames.org/ontology/
Giunchiglia, F., Kharkevich, U., Zaihrayeu, I.: Concept Search. In: Aroyo, L., Traverso, P., Ciravegna, F., Cimiano, P., Heath, T., Hyvönen, E., Mizoguchi, R., Oren, E., Sabou, M., Simperl, E. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 429–444. Springer, Heidelberg (2009)
Haber, E.M., Ioannidis, Y.E., Livny, M.: Foundations of Visual Metaphors for Schema Display. J. Intell. Inf. Syst. 3(3/4), 263–298 (1994)
Hearst, M.: Search User Interfaces. Cambridge University Press, Cambridge (2009), ISBN 9780521113793
IBM. Manyeyes (December 2010), http://www-958.ibm.com/software/data/cognos/manyeyes/
Johansson, S.: Visual Exploration of Categorical and Mixed Data Sets. In: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration, Paris, France, July 28-28, pp. 21–29 (2009)
Johnson, B., Shneiderman, B.: Treemaps: A Space-Filling Approach to the Visualization of Hierarchical Information Structures. In: Proceedings of the IEEE Information Visualization 1991, pp. 275–282. IEEE, Los Alamitos (1991)
Maceachren, A.M.: An Evolving Cognitive-Semiotic Approach to Geographic Visualization and Knowledge Construction. Cartography and Geographic Information Systems 19, 197–200 (2001)
Mackinlay, J.D.: Automatic Design of Graphical Presentations, Ph.D. Thesis, Department of Computer Science, Stanford University (1986)
Mackinlay, J.D., Hanrahan, P., Stolte, C.: Show Me: Automatic Presentation for Visual Analysis. IEEE Trans. Vis. Comput. Graph 13(6), 1137–1144 (2007)
North, C.: A Taxonomy of Information Visualization User-Interfaces (1998), http://www.cs.umd.edu/~north/infoviz.html
OLIVE: On-line Library of Information Visualization Environments, University of Maryland College Park (1999), http://otal.umd.edu/Olive/
Rajaraman, A.: Kosmix: High Performance Topic Exploration Using the Deep Web. In: Proceedings of the VLDB Endowment, August 2008, vol. 2(1), pp. 1524–1529 (2009)
Rodrigues Jr., J.F., Traina, A.J.M., de Oliveira, M.C.F., Traina Jr., C.: Reviewing Data Visualization: An Analytical Taxonomical Study. In: Proceedings of the Information Visualization, IV 2006 (2006)
Shneiderman, B.: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In: IEEE Symp. Visual Languages, pp. 336–343. IEEE CS Press, Los Alamitos (1996)
Stevens, S.S.: On the Theory of Scales of Measurement. Science 103(2684), 677–680 (1946), http://web.duke.edu/philosophy/bio/Papers/Stevens_Measurement.pdf
Stolte, C., Tang, D., Hanrahan, P.: Polaris: A System for Query, Analysis, and Visualization of Multidimensional Databases. Commun. ACM 51(11), 75–84 (2008)
Takatsuka, M., Gahegan, M.: GeoVISTA Studio: A Codeless Visual Programming Environment for Geoscientific Data Analysis and Visualization. Computational Geoscience 28, 1131–1144 (2002)
Ward, M.O.: A Taxonomy of Glyph Placement Strategies for Multidimensional Data Visualization. Journal of Information Visualization 1(3/4)
Wattenberg, M.: Visual Exploration of Multivariate Graphs. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI 2006, pp. 811–819. ACM, New York (2006)
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Bozzon, A., Brambilla, M., Catarci, T., Ceri, S., Fraternali, P., Matera, M. (2011). Visualization of Multi-domain Ranked Data. In: Ceri, S., Brambilla, M. (eds) Search Computing. Lecture Notes in Computer Science, vol 6585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19668-3_6
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DOI: https://doi.org/10.1007/978-3-642-19668-3_6
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