The VLDB Journal

, Volume 25, Issue 4, pp 597–622 | Cite as

A holistic and principled approach for the empty-answer problem

  • Davide Mottin
  • Alice Marascu
  • Senjuti Basu Roy
  • Gautam Das
  • Themis Palpanas
  • Yannis Velegrakis
Regular Paper
  • 360 Downloads

Abstract

We propose a principled optimization-based interactive query relaxation framework for queries that return no answers. Given an initial query that returns an empty-answer set, our framework dynamically computes and suggests alternative queries with fewer conditions than those the user has initially requested, in order to help the user arrive at a query with a non-empty-answer, or at a query for which no matter how many additional conditions are ignored, the answer will still be empty. Our proposed approach for suggesting query relaxations is driven by a novel probabilistic framework based on optimizing a wide variety of application-dependent objective functions. We describe optimal and approximate solutions of different optimization problems using the framework. Moreover, we discuss two important extensions to the base framework: the specification of a minimum size on the number of results returned by a relaxed query and the possibility of proposing multiple conditions at the same time. We analyze the proposed solutions, experimentally verify their efficiency and effectiveness, and illustrate their advantages over the existing approaches.

Keywords

Database Database usability Query modification Empty-answer problem 

References

  1. 1.
    Agrawal, S., Chaudhuri, S., Das, G., Gionis, A.: Automated ranking of database query results. In: CIDR (2003)Google Scholar
  2. 2.
    Ahlberg, C., Shneiderman, B.: The alphaslider: a compact and rapid selector. In: CHI, p. 226 (1994)Google Scholar
  3. 3.
    Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A.: An optimization framework for query recommendation. In: WSDM, pp. 161–170 (2010)Google Scholar
  4. 4.
    Arai, B., Das, G., Gunopulos, D., Koudas, N.: Anytime measures for top-k algorithms on exact and fuzzy data sets. VLDB J. 18(2), 407–427 (2009)CrossRefGoogle Scholar
  5. 5.
    Baeza-Yates, R.A., Hurtado, C.A., Mendoza, M.: Query recommendation using query logs in search engines. In: EDBT Workshops, pp. 588–596 (2004)Google Scholar
  6. 6.
    Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval. Addison-Wesley, New York (2011)Google Scholar
  7. 7.
    Basu Roy, S., Wang, H., Das, G., Nambiar, U., Mohania, M.: Minimum-effort driven dynamic faceted search in structured databases. In: CIKM, pp. 13–22 (2008)Google Scholar
  8. 8.
    Bishop, Y.M.M., Fienberg, S.E., Holland, P.W.: Discr. Multivariate Analysis: Theory and Practice. MIT Press, Cambridge (1975)MATHGoogle Scholar
  9. 9.
    Bosc, P., HadjAli, A., Pivert, O.: Empty versus overabundant answers to flexible relational queries. Fuzzy Sets Syst. 159(12), 1450–1467 (2008)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Bosc, P., HadjAli, A., Pivert, O.: Incremental controlled relaxation of failing flexible queries. JIIS 33(3), 261–283 (2009)Google Scholar
  11. 11.
    Chang, Y., Ounis, I., Kim, M.: Query reformulation using automatically generated query concepts from a document space. Inf. Process. Manag. 42(2), 453–468 (2006)CrossRefGoogle Scholar
  12. 12.
    Chapman, A., Jagadish, H.V.: Why not? In: SIGMOD, pp. 523–534 (2009)Google Scholar
  13. 13.
    Chaudhuri, S.: Generalization and a framework for query modification. In: ICDE, pp. 138–145 (1990)Google Scholar
  14. 14.
    Chaudhuri, S., Das, G., Hristidis, V., Weikum, G.: Probabilistic ranking of database query results. In: VLDB, pp. 888–899 (2004)Google Scholar
  15. 15.
    Chaudhuri, S., Das, G., Hristidis, V., Weikum, G.: Probabilistic information retrieval approach for ranking of database query results. TODS 31(3), 1134–1168 (2006)CrossRefGoogle Scholar
  16. 16.
    Chomicki, J., Ciaccia, P., Meneghetti, N.: Skyline queries, front and back. SIGMOD Rec. 42(3), 6–18 (2013)CrossRefGoogle Scholar
  17. 17.
    Chu, W.W., Chen, Q.: Neighborhood and associative query answering. J. Intell. Inf. Syst. 1(3/4), 355–382 (1992)CrossRefGoogle Scholar
  18. 18.
    Domingo, C., Mishra, N., Pitt, L.: Efficient read-restricted monotone CNF/DNF dualization by learning with membership queries. Mach. Learn. 37(1), 89–110 (1999)CrossRefMATHGoogle Scholar
  19. 19.
    Gaasterland, T.: Cooperative answering through controlled query relaxation. IEEE Expert 12(5), 48–59 (1997)CrossRefGoogle Scholar
  20. 20.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: a guide to the theory of NP-completeness (1990)Google Scholar
  21. 21.
    Gauch, S., Smith, J.: Search improvement via automatic query reformulation. TOIS 9(3), 249–280 (1991)CrossRefGoogle Scholar
  22. 22.
    Gauch, S., Smith, J.B.: An expert system for automatic query reformulation. JASIS 44(3), 124–136 (1993)CrossRefGoogle Scholar
  23. 23.
    Godfrey, P.: Minimization in cooperative response to failing database queries. Int. J. Coop. Inf. Syst. 6(2), 95–149 (1997)CrossRefGoogle Scholar
  24. 24.
    Greene, S., Tanin, E., Plaisant, C., Shneiderman, B., Olsen, L., Major, G., Johns, S.: The end of zero-hit queries: query previews for NASA’s global change master directory. Int. J. Digit. Libr. 2(2–3), 79–90 (1999)CrossRefGoogle Scholar
  25. 25.
    Hristidis, V., Hu, Y., Ipeirotis, P.G.: Ranked queries over sources with boolean query interfaces without ranking support. In: ICDE, pp. 872–875 (2010)Google Scholar
  26. 26.
    Janas, J.M.: On the feasibility of informative answers. In: Gallaire, H., Minker, J., Nicolas, J. (eds.) Advances in Data Base Theory, pp. 397–414. Springer, New York (1981)CrossRefGoogle Scholar
  27. 27.
    Jannach, D.: Techniques for fast query relaxation in content-based recommender systems. In: KI’06: Advances in AI, pp. 49–63 (2007)Google Scholar
  28. 28.
    Jannach, D., Liegl, J.: Conflict-directed relaxation of constraints in content-based recommender systems. In: Advances in Applied AI, pp. 819–829 (2006)Google Scholar
  29. 29.
    Junker, U.: QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems. AAAI 4, 167–172 (2004)Google Scholar
  30. 30.
    Kashyap, A., Hristidis, V., Petropoulos, M.: Facetor: cost-driven exploration of faceted query results. In: CIKM (2010)Google Scholar
  31. 31.
    Koudas, N., Li, C., Tung, A.K.H., Vernica, R.: Relaxing join and selection queries. In: VLDB, pp. 199–210 (2006)Google Scholar
  32. 32.
    Li, C., Yan, N., Roy, S.B., Lisham, L., Das, G.: Facetedpedia: dynamic generation of query-dependent faceted interfaces for wikipedia. In: WWW, pp. 651–660 (2010)Google Scholar
  33. 33.
    Luo, G.: Efficient detection of empty-result queries. In: VLDB, pp. 1015–1025 (2006)Google Scholar
  34. 34.
    McSherry, D.: Incremental relaxation of unsuccessful queries. In: ECCBR, pp. 331–345 (2004)Google Scholar
  35. 35.
    Mishra, C., Koudas, N.: Interactive query refinement. In: EDBT, pp. 862–873. ACM (2009)Google Scholar
  36. 36.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)Google Scholar
  37. 37.
    Motro, A.: Seave: a mechanism for verifying user presuppositions in query systems. TOIS 4(4), 312–330 (1986)CrossRefGoogle Scholar
  38. 38.
    Motro, A.: Vague: a user interface to relational databases that permits vague queries. TOIS 6(3), 187–214 (1988)CrossRefGoogle Scholar
  39. 39.
    Motro, A.: Flex: a tolerant and cooperative user interface to databases. TKDE 2(2), 231–246 (1990)Google Scholar
  40. 40.
    Mottin, D., Marascu, A., Roy, S.B., Das, G., Palpanas, T., Velegrakis, Y.: A probabilistic optimization framework for the empty-answer problem. PVLDB 6(14), 1762–1773 (2013)Google Scholar
  41. 41.
    Mottin, D., Marascu, A., Basu Roy, S., Das, G., Palpanas, T., Velegrakis, Y.: IQR: an interactive query relaxation system for the empty-answer problem. In: SIGMOD, pp. 1095–1098, ACM (2014)Google Scholar
  42. 42.
    Muslea, I.: Machine learning for online query relaxation. In: KDD, pp. 246–255 (2004)Google Scholar
  43. 43.
    Muslea, I., Lee, T.J.: Online query relaxation via bayesian causal structures discovery. In: AAAI, pp. 831–836 (2005)Google Scholar
  44. 44.
    Palpanas, T., Koudas, N.: Entropy based approximate querying and exploration of datacubes. In: SSDBM, pp. 81–90 (2001)Google Scholar
  45. 45.
    Palpanas, T., Koudas, N., Mendelzon, A.O.: Using datacube aggregates for approximate querying and deviation detection. IEEE Trans. Knowl. Data Eng. 17(11), 1465–1477 (2005)CrossRefGoogle Scholar
  46. 46.
    Pei, J., Jin, W., Ester, M., Tao, Y.: Catching the best views of skyline: a semantic approach based on decisive subspaces. In: VLDB, pp. 253–264 (2005)Google Scholar
  47. 47.
    Plaisant, C., Shneiderman, B., Doan, K., Bruns, T.: Interface and data architecture for query preview in networked information systems. ACM Trans. Inf. Syst. 17(3), 320–341 (1999)CrossRefGoogle Scholar
  48. 48.
    Radlinski, F., Joachims, T.: Query chains: learning to rank from implicit feedback. In: KDD, pp. 239–248. ACM (2005)Google Scholar
  49. 49.
    Ras, Z.W., Dardzinska, A.: Solving failing queries through cooperation and collaboration. WWW 9(2), 173–186 (2006)CrossRefGoogle Scholar
  50. 50.
    Singh, G., Parikh, N., Sundaresan, N.: Rewriting null e-commerce queries to recommend products. In: WWW (2012)Google Scholar
  51. 51.
    Tran, Q.T., Chan, C.-Y.: How to conquer why-not questions. In: SIGMOD, pp. 15–26 (2010)Google Scholar
  52. 52.
    Wen, J.-R., Nie, J.-Y., Zhang, H.: Query clustering using user logs. TOIS 20(1), 59–81 (2002)CrossRefGoogle Scholar
  53. 53.
    Zhang, X., Chomicki, J.: Preference queries over sets. In: ICDE, pp. 1019–1030 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Hasso Plattner InstitutePotsdamGermany
  2. 2.University of TrentoTrentoItaly
  3. 3.IBM Research-IrelandDublinIreland
  4. 4.Department of Computer ScienceNew Jersey Institute of TechnologyNewarkUSA
  5. 5.University of Texas ArlingtonArlingtonUSA
  6. 6.QCRIDohaQatar
  7. 7.University of Paris DescartesParisFrance

Personalised recommendations