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User Exploration of Search Space Using Tradeoffs

  • Zachi Baharav
  • David S. Gladstein
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 529)

Abstract

We describe a system for representing search results as an interactive graph, in contrast to the common static representation as an ordered list. The search terms are represented as Key-Nodes, and the results are represented as connected Record-Nodes. The user can then explore tradeoffs by assigning different importance values to Key-Nodes. We suggest algorithms for placement of Record-Nodes in the graph to ensure both smooth change of the representation in response to changes in user preferences, and clustering of results. The work was implemented using Haskell and a client-side Web interface.

Keywords

Search Graph Graph representation Tradeoff 

References

  1. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1), 107–117 (1998)CrossRefGoogle Scholar
  2. Page, L., Brin, S., Rajeev, M., Terry, W.: The PageRank Citation Ranking: Bringing Order to the Web. Stanford University, Technical report (1998)Google Scholar
  3. Gansner, E.R., Koren, Y., North, S.C.: Graph drawing by stress majorization. In: Pach, J. (ed.) GD 2004. LNCS, vol. 3383, pp. 239–250. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  4. Dwyer, T., Koren, Y., Marriott, K.: IPSep-CoLa: an incremental procedure for separation constraint layout of graphs. IEEE Trans. Vis. Comput. Graph. 12(5), 821–828 (2006)CrossRefGoogle Scholar
  5. Hearst, M.A.: Search User Interfaces. Cambridge University Press, (2009). Also available on the web http://searchuserinterfaces.com/book/
  6. Parameswaran, A., et al.: Human-assisted graph search: it’s okay to ask questions. Proc. VLDB Endowment 4(5), 267–278 (2011)MathSciNetCrossRefGoogle Scholar
  7. Marriott, K., Purchase, H., Wybrow, M., Goncu, C.: Memorability of visual features in network diagrams. IEEE Trans. Vis. Comput. Graph. 18(12), 2477–2485 (2012)CrossRefGoogle Scholar
  8. Stolper, C.D., Kahng, M., Lin, Z., Foerster, F., Goel, A., Horng, D.: GLOs: Graph-level operations for exploratory network visualization. In: CHI 2014 Extended Abstracts on Human Factors in Computing Systems, pp. 1375–1380. ACM (2014)Google Scholar
  9. Dwyer, T., Mears, C., Morgan, K., Niven, T., Marriott, K., Wallace, M.: Improved optimal and approximate power graph compression for clearer visualization of dense graphs. In: Pacific Visualization Symposium (PacificVis), pp. 105–112. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Cogswell Polytechnical CollegeSunnyvaleUSA

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