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A Comparative Evaluation of 2D And 3D Visual Exploration of Document Search Results

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Information Retrieval Technology (AIRS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8870))

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Abstract

This work presents and experimental comparison between 2D and 3D search and visualization platforms. The main objective of the study is two explore the following two research questions: what method is most robust in terms of the success rate? And, what method is faster in terms of average search time? The obtained results show that, although successful rates and subject preferences are higher for 3D search and visualization, search times are still lower for 2D search and visualization.

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References

  1. Bush, V.: As We Think. Atlantic Monthly 176, 101–108 (1945)

    Google Scholar 

  2. Salton, G.: The SMART Retrieval System—Experiments in Automatic Document Retrieval. Prentice Hall Inc., Englewood Cliffs (1971)

    Google Scholar 

  3. Jones, K.S.: Automatic Keyword Classification for Information Retrieval, Butterworths, London (1971)

    Google Scholar 

  4. Sparck Jones, K.: A Statistical Interpretation of Term Specificity and its Application in Retrieval. Journal of Documentation 28, 11–21 (1972)

    Article  Google Scholar 

  5. Salton, G., Wong, A., Yang, C.S.: A Vector Space Model for Information Retrieval. Communications of the ACM 18(11), 613–620 (1975)

    Article  MATH  Google Scholar 

  6. Kruskal, J.B., Wish, M.: Multidimensional Scaling, Sage University Paper series on Quantitative Application in the Social Sciences, pp. 7–11. Sage Publications, Beverly Hills (1978)

    Google Scholar 

  7. Kohonen, T., Kaski, S., Lagus, K., Salojärvi, J., Honkela, J., Paatero, V., Saarela, A.: Self Organization of a Massive Document Collection. IEEE Transaction on Neural Networks 11(3), 574–585 (2000)

    Article  Google Scholar 

  8. Lagus, K., Kaski, S., Kohonen, T.: Mining Massive Document Collections by the WEBSOM Method. Information Sciences 163(1-3), 135–156 (2004)

    Article  Google Scholar 

  9. Hinton, G.E., Salakhutdinov, R.R.: Reducing the Dimensionality of Data with Neural Networks. Science 313, 504–507 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  10. Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer Series in Statistics (2002)

    Google Scholar 

  11. Deerwester, S., et al.: Improving Information Retrieval with Latent Semantic Indexing. In: Proceedings of the 51st Annual Meeting of the American Society for Information Science (1988)

    Google Scholar 

  12. Correa, R.F., Ludermir, T.B.: Dimensionality Reduction of Very Large Document Collections by Semantic Mapping. In: Proceedings of 6th Int. Workshop on Self-Organizing Maps (2007)

    Google Scholar 

  13. Feldman, R., Klosgen, W., Zilberstein, A.: Visualization Techniques to Explore Data Mining Results for Document Collections. In: Proceedings of the Third Annual Conference on Knowledge Discovery and Data Mining (KDD), Newport Beach (1997)

    Google Scholar 

  14. Newman, D., Baldwin, T., Cavedon, L., Huang, E., Karimi, S., Martinez, D., Scholer, F., Zobel, J.: Visualizing Search Results and Document Collections using Topic Maps. Web Semantics: Science, Services and Agents on the World Wide Web 8 (2010)

    Google Scholar 

  15. Kandogan, E.: Visualizing Multi-dimensional Clusters, Trends, and Outliers using Star Coordinates. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2001), pp. 107–116 (2001)

    Google Scholar 

  16. Olsen, K.A., Korfhage, R.R., Sochats, K.M., Spring, M.B., Williams, J.G.: Visualization of a Document Collection: The Vibe System. Information Processing & Management 29(1), 69–81 (1993)

    Article  Google Scholar 

  17. Wise, J.A., Thomas, J.J., Pennock, K.A., Lantrip, D.B., Pottier, M.C., Schur, A., Crow, V.: Visualizing the Non-visual: Spatial Analysis and Interaction with Information from Text Documents. In: Proceedings of the IEEE Symposium on Information Visualization (INFOVIS 1995). IEEE Computer Society, Washington, DC (1995)

    Google Scholar 

  18. Benford, S., Snowdown, D., Greenhalgh, C., Ingram, R., Knox, I., Brown, C.: VR-VIBE: A Visual Environment for Co-operative Information Retrieval. In: Eurographics 1995, pp. 349–360 (1995)

    Google Scholar 

  19. Morse, E.L., Lewis, M.: Why Information Retrieval Visualization Sometimes Fail. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 1680–1685 (1997)

    Google Scholar 

  20. Crow, V., Pottier, M., Thomas, J., Lantrip, D., Struble, C., Pennock, K., Schur, A., Wise, J., Fiegel, T., York, J.: Multidimensional Visualization and Browsing for Intelligence Analysis. Technical Report, Pacific Northwest Lab, Richland, WA, United States (1994)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Infocomm Research, Human Language Technology, Singapore

    Rafael E. Banchs

Authors
  1. Rafael E. Banchs
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Editor information

Editors and Affiliations

  1. Institute of Visual Informatic, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Azizah Jaafar

  2. Institute of Visual Informatics, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Nazlena Mohamad Ali

  3. Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Shahrul Azman Mohd Noah

  4. Insight Centre for Data Analytics, Dublin City University, Glasnevin, 9, Dublin, Ireland

    Alan F. Smeaton

  5. Information Systems, Queensland University of Technology, 4001, Brisbane, QLD, Australia

    Peter Bruza

  6. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

    Zainab Abu Bakar  & Nursuriati Jamil  & 

  7. Cyber Security Center, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, 57000, Kuala Lumpur, Malaysia

    Tengku Mohd Tengku Sembok

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© 2014 Springer International Publishing Switzerland

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Banchs, R.E. (2014). A Comparative Evaluation of 2D And 3D Visual Exploration of Document Search Results. In: Jaafar, A., et al. Information Retrieval Technology. AIRS 2014. Lecture Notes in Computer Science, vol 8870. Springer, Cham. https://doi.org/10.1007/978-3-319-12844-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-12844-3_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12843-6

  • Online ISBN: 978-3-319-12844-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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