Dealing with Empty and Overabundant Answers to Flexible Queries

Samyr  Abrahão Moises; Silvio do  Lago Pereira

doi:10.4236/jdaip.2014.21003

Samyr Abrahão Moises, Silvio do Lago Pereira

Abstract: In traditional database applications, queries intend to retrieve data satisfying precise conditions. As a result, thousands of data can be retrieved (overabundant answer) or, even worse, no data at all (empty answer). In both cases, the queries must be reformulated to produce more significant results and, typically, many related queries are submitted by a user before he can be finally satisfied. To overcome these problems, this paper proposes a unified solution in the framework of flexible queries with fuzzy semantics. This solution, based on the concept of semantic proximity and implemented in a tool for flexible query answering, allows the automatic reformulation of queries with empty or overabundant answers.

Keywords: Relational Database; Fuzzy Logic; Flexible Query; Empty Answer; Overabundant Answer

Cite this paper: S. Abrahão Moises and S. Lago Pereira, "Dealing with Empty and Overabundant Answers to Flexible Queries," Journal of Data Analysis and Information Processing, Vol. 2 No. 1, 2014, pp. 12-18. doi: 10.4236/jdaip.2014.21003.

References

[1] S. Abiteboul, R. Hull and V. Vianu, “Foundation of Databases,” Addison-Wesley, Boston, 1994.

[2] A. G. Maguitman, “Intelligent Support for Knowledge Capture and Construction,” Ph.D. Dissertation, Indiana University, Indianapolis, 2004.

[3] D. H. Lee and M. H. Kim, “Accommodating Subjective Vagueness through a Fuzzy Extension to the Relational Data Model,” Information Systems, Vol. 18, No. 6, 1993, pp. 363-374. http://dx.doi.org/10.1016/0306-4379(93)90013-Q

[4] J. Galindo, A. Urrutia and M. Piattini, “Fuzzy Databases: Modeling, Design and Implementation,” Idea Group Publishing, Hershey, 2006.

[5] Z. M. Ma and L. Yan, “A Literature Overview of Fuzzy Database Models,” Journal of Information Science and Engineering, Vol. 24, No. 1, 2008, pp.189-202.

[6] L. A. Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8, No. 3, 1965, pp. 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X

[7] P. Bosc, A. Hadjali and O. Pivert, “Weakening of Fuzzy Relational Queries: An Absolute Proximity Relation- Based Approach,” Mathware & Soft Computing, Vol. 14, No. 1, 2007, pp. 35-55.

[8] T. Gausterland, “Cooperative Answering through Controlled Query Relaxation,” IEEE Expert, Vol. 12, No. 5, 1997, pp. 48-59. http://dx.doi.org/10.1109/64.621228

[9] I. Muslea, “Machine Learning for Online Query Relaxation,” 10th International Conference of Knowledge and Discovery and Data Mining, Washington DC, 2004, pp. 246-255.

[10] T. Andreasen and O. Pivert, “On the Weakening of Fuzzy Relational Queries,” First 8th International Symposium on Methods for Intelligence System, Charlote, October 1994, pp. 144-153.

[11] P. Bosc, A. Hadjali and O. Pivert, “About Overabundant Answers to Flexible Queries,” Proceedings of the 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU’06), Paris, 2-7 July 2006, pp. 2221-2228.

[12] P. Bosc, A. Hadjali and O. Pivert, “Empty versus Overabundant Answers to Flexible Relational Queries,” Fuzzy Sets and Systems, Vol. 159, No. 12, 2008, pp. 1450-1467. http://dx.doi.org/10.1016/j.fss.2008.01.007

[13] A. Hadj Ali, D. Dubois and H. Prade, “Qualitative Reasoning Based on Fuzzy Relative Orders of Magnitude,” IEEE Transactions on Fuzzy Systems, Vol. 11, No. 1, 2003, pp. 9-23. http://dx.doi.org/10.1109/TFUZZ.2002.806313

[14] L. Wang, “A Course in Fuzzy Systems and Control,” Prentice-Hall, Upper Saddle River, 1997.

[15] E. E. Kerre and M. de Cock, “Linguistic Modifiers: An Overview,” In: G. Chen, M. Ying and K.-Y. Cai, Eds., Fuzzy Logic and Soft Computing, Vol. 9, Kluwer Academic Publishers, Norwell, 1999, pp. 69-85.

[16] I. Bratko, “Prolog Programming for Artificial Intelligence,” 4th Edition, Pearson, London, 2011.

[17] Oracle, “MySQL 5.6 Reference Manual.” http://dev.mysql.com/doc/refman