A Design of Faceted Search Engine – a Review

  • Authors

    • Mohammed Najah Mahdi
    • Roslan Ismail
    • Abdul Rahim Ahmad
    • Kavintheran Thambiratnam
    • Mohammed Abdulameer Mohammed
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.20.20595
  • Information Retrieval, Search Engine, Exploratory Search, Faceted Search Engine.

  • The World Wide Web (WWW) allows the people to share information and data from large database repositories globally. The amount of information is already in the billions of databases. We need to search the information with specialize tools known generically as search engine (SE). With the huge data that needs to be handled, search engines need to retrieve meaningful information intelligently, whereby only information of interest to the searcher needs to be returned. Facets (the particular aspect or feature of something being searched) can play an important role in helping the user understand an information space better. Queries techniques within faceted search will make the search results immediate and the interaction between searcher and search engine uninterrupted and focused. They can contribute to the user’s understanding of the researched terms or topics. Furthermore, they are more fun and interesting to use because users directly manipulate the search controls and the results can be displayed through choices of presentation such as text displays, transition animations, graphs etc. which bring the process closer to an experience in game playing. This paper review the design of faceted search engine.

     

  • References

    1. [1] J. Curran, N. Fenton, and D. Freedman, Misunderstanding the internet: Routledge, 2016.

      [2] A. Selcuk, C. Örencik, and E. Savas, "Private search over big data leveraging distributed file system and parallel processing," 2015.

      [3] B. Kules and B. Shneiderman, "Users can change their web search tactics: Design guidelines for categorized overviews," Information Processing & Management, vol. 44, pp. 463-484, 2008.

      [4] D. Bakrola and S. Gandhi, "Enhancing Web Search Results Using Aggregated Search," in Proceedings of International Conference on ICT for Sustainable Development, 2016, pp. 675-688.

      [5] N. Ibrahim, A. H. Chaibi, and H. B. Ghézala, "Scientometric re-ranking approach to improve search results," Procedia Computer Science, vol. 112, pp. 447-456, 2017.

      [6] A. N. Langville and C. D. Meyer, Google's PageRank and beyond: The science of search engine rankings: Princeton University Press, 2011.

      [7] C. D. Manning, P. Raghavan, and H. Schütze, "Introduction to information retrieval," ed: Cambridge University Press, 2008.

      [8] S. Mizzaro, "Relevance: The whole history," JASIS, vol. 48, pp. 810-832, 1997.

      [9] T. Saracevic, "Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance," Journal of the American Society for Information Science and Technology, vol. 58, pp. 2126-2144, 2007.

      [10] A. Singhal, "Modern information retrieval: A brief overview," IEEE Data Eng. Bull., vol. 24, pp. 35-43, 2001.

      [11] D. Wolfram, A. Spink, B. J. Jansen, and T. Saracevic, "Vox populi: The public searching of the web," JASIST, vol. 52, pp. 1073-1074, 2001.

      [12] "PubMed," 2017.

      [13] G. Salton, A. Wong, and C.-S. Yang, "A vector space model for automatic indexing," Communications of the ACM, vol. 18, pp. 613-620, 1975.

      [14] K. Sparck Jones, "A statistical interpretation of term specificity and its application in retrieval," Journal of documentation, vol. 28, pp. 11-21, 1972.

      [15] J. M. Kleinberg, "Authoritative sources in a hyperlinked environment," Journal of the ACM (JACM), vol. 46, pp. 604-632, 1999.

      [16] S. Brin and L. Page, "The anatomy of a large-scale hypertextual web search engine," Computer networks, vol. 56, pp. 3825-3833, 1998.

      [17] J. Park and S.-H. Yook, "Bayesian Inference of Natural Rankings in Incomplete Competition Networks," Scientific Reports, vol. 4, p. 6212, 08/28/online 2014.

      [18] K. Järvelin and J. Kekäläinen, "Cumulated gain-based evaluation of IR techniques," ACM Transactions on Information Systems (TOIS), vol. 20, pp. 422-446, 2002.

      [19] R. Caruana, A. Niculescu-Mizil, G. Crew, and A. Ksikes, "Ensemble selection from libraries of models," in Proceedings of the twenty-first international conference on Machine learning, 2004, p. 18.

      [20] M. J. Bates, "The design of browsing and berrypicking techniques for the online search interface," Online review, vol. 13, pp. 407-424, 1989.

      [21] G. Marchionini, "Exploratory search: from finding to understanding," Communications of the ACM, vol. 49, pp. 41-46, 2006.

      [22] E. Goodman, M. Kuniavsky, and A. Moed, "Observing the user experience," Burlington, Massachusetts: Morgan Kaufmann, 2012.

      [23] J. Nielsen, "Usability 101: Introduction to usability," ed, 2003.

      [24] R. W. White and R. A. Roth, "Exploratory search: beyond the query-response paradigm (Synthesis lectures on information concepts, retrieval & services)," Morgan and Claypool Publishers, vol. 3, 2009.

      [25] J. Nielsen, "Guerrilla HCI: Using discount usability engineering to penetrate the intimidation barrier," Cost-justifying usability, pp. 245-272, 1994.

      [26] S. Ben and P. Catherine, "Designing the user interface,")^(Eds.):‘Book Designing the user interface’(Reading, Mass.: Addison Wesley Longman, 1998, edn.), 2005.

      [27] K. Franzen and J. Karlgren, "Verbosity and interface design," SICS Research Report, 2000.

      [28] M. Hassenzahl, "The interplay of beauty, goodness, and usability in interactive products," Human-computer interaction, vol. 19, pp. 319-349, 2004.

      [29] T. Ben-Bassat, J. Meyer, and N. Tractinsky, "Economic and subjective measures of the perceived value of aesthetics and usability," ACM Transactions on Computer-Human Interaction (TOCHI), vol. 13, pp. 210-234, 2006.

      [30] A. Aizpurua, M. Arrue, and M. Vigo, "Prejudices, memories, expectations and confidence influence experienced accessibility on the Web," Computers in Human Behavior, vol. 51, pp. 152-160, 2015.

      [31] H. C. L. Hsieh and N. C. Cheng, "A Theoretical Model for the Design of Aesthetic Interaction," in International Conference on Human-Computer Interaction, 2016, pp. 178-187.

      [32] G. Hotchkiss, T. Sherman, R. Tobin, C. Bates, and K. Brown, "Search engine results: 2010," Enquiro Search Solutions, pp. 1-61, 2010.

  • Downloads

  • How to Cite

    Najah Mahdi, M., Ismail, R., Rahim Ahmad, A., Thambiratnam, K., & Abdulameer Mohammed, M. (2018). A Design of Faceted Search Engine – a Review. International Journal of Engineering & Technology, 7(3.20), 489-493. https://doi.org/10.14419/ijet.v7i3.20.20595