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Query recommendation in digital libraries using OLAP

Published:06 June 2010Publication History

ABSTRACT

Query suggestion is well-known to enhance the user's search for relevant documents. In this work, we propose a novel technique that emulates a human skill when searching or exploring digital collections. In general, a user begins searching by providing a naïve query and then analyzes the retrieved documents in order to refine the query search. We decided to emulate this behavior by generating alternative queries using OLAP. Such queries are the result of performing multiple data summarizations on digital libraries, and then generating cuboids depending on the correlation between the keywords of the collection and the subset of keywords belonging to the previous search. Moreover, we introduce techniques to efficiently obtain query suggestions inside the DBMS by exploiting UDFs and SQL queries.

References

  1. ]]Y. Chen, W. Wang, and X. Lin Z. Liu. Keyword Search on Structured and Semi-structured Data. Tutorial Description. In SIGMOD, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. ]]Z. Chen, C. Ordonez, and C. Garcia-Alvarado. Fast and Dynamic OLAP Exploration Using UDFs. In SIGMOD, pages 1087--1090, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. ]]C. Garcia-Alvarado and C. Ordonez. Information retrieval from digital libraries in SQL. In WIDM Workshop, pages 55--62, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. ]]C. Ordonez and Z. Chen. Evaluating Statistical Tests on OLAP Cubes to Compare Degree of Disease. IEEE TITB, 13(5):756--765, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      KEYS '10: Proceedings of the 2nd International Workshop on Keyword Search on Structured Data
      June 2010
      28 pages
      ISBN:9781450301879
      DOI:10.1145/1868366

      Copyright © 2010 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 6 June 2010

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