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IQR: an interactive query relaxation system for the empty-answer problem

Published:18 June 2014Publication History

ABSTRACT

We present IQR, a system that demonstrates optimization based interactive relaxations for queries that return an empty answer. Given an empty answer, IQR dynamically suggests one relaxation of the original query conditions at a time to the user, based on certain optimization objectives, and the user responds by either accepting or declining the relaxation, until the user arrives at a non-empty answer, or a non-empty answer is impossible to achieve with any further relaxations. The relaxation suggestions hinge on a proba- bilistic framework that takes into account the probability of the user accepting a suggested relaxation, as well as how much that relaxation serves towards the optimization objec- tive. IQR accepts a wide variety of optimization objectives - user centric objectives, such as, minimizing the number of user interactions (i.e., effort) or returning relevant results, as well as seller centric objectives, such as, maximizing profit. IQR offers principled exact and approximate solutions for gen- erating relaxations that are demonstrated using multiple, large real datasets.

References

  1. S. Agrawal, S. Chaudhuri, G. Das, and A. Gionis. Automated ranking of database query results. In CIDR, 2003.Google ScholarGoogle Scholar
  2. B. Arai, G. Das, D. Gunopulos, and N. Koudas. Anytime measures for top-k algorithms on exact and fuzzy data sets. VLDB J.,18 (2):407--427,2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. R. A. Baeza-Yates and B. A. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley, NewYork, 2011.Google ScholarGoogle Scholar
  4. Y. Bishop, S. Fienberg, and P. Holland. Discr. Multivariate Analysis: Theory and Practice.MIT Press, 1975.Google ScholarGoogle Scholar
  5. S. Chaudhuri, G. Das, V. Hristidis, and G. Weikum. Probabilistic information retrieval approach for ranking of database query results. TODS, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. D. Mottin, A. Marascu, S. B. Roy, G. Das, T. Palpanas, and Y. Velegrakis. A probabilistic optimization framework for the empty-answer problem. PVLDB, 6 (14):1762--1773,2013. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data
      June 2014
      1645 pages
      ISBN:9781450323765
      DOI:10.1145/2588555

      Copyright © 2014 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 18 June 2014

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      SIGMOD '14 Paper Acceptance Rate107of421submissions,25%Overall Acceptance Rate785of4,003submissions,20%

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