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Fuzzy SQL queries in standard SQL database

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Treść / Zawartość
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Języki publikacji
EN
Abstrakty
EN
Uncertain queries are very common in many areas of human activity. The problem can be seen particularly in medicine, where expressions like ”very high”, ”low”, ”normal” are commonly used in order to describe different information. However, the most popular data repositories do not allow to form imprecise queries in order to filter information. Therefore, the paper proposes an extension to the standard SQL language allowing anybody to profit from fuzzy database using any SQL engine. The existing approaches employ different mechanisms in order to allow the user to perform fuzzy queries on a database. The most complex solutions modify the database engine itself. However, such approach is strongly bound to the modified server version and must be updated with any development of the original server. Nevertheless, there is possible to store fuzzy information using for instance columns of regular relational database. Therefore, this approach proposes extensions to the query language allowing to use fuzzy information in a query and provides a parser transforming a fuzzy query into a standard SQL. Thus, the database server version is irrelevant. The solution is provided as a module written in multi-platform Java language using popular JDBC database connection.
Słowa kluczowe
Rocznik
Tom
Strony
28--37
Opis fizyczny
Bibliogr. 14 poz., rys., tab., wykr.
Twórcy
autor
  • University of Silesia, Institute of Computer Science, Będzińska 39, 41-200 Sosnowiec, Poland
Bibliografia
  • [1] BALAMURUGAN V., KANNAN K. S. A framework for computing linguistic hedges. International Journal of Database Management Systems, 2010, Vol. 2.
  • [2] BOSC P., LIÉTARD L., PIVERT O. Sugeno fuzzy integral as a basis for the interpretation of flexible queries involving monotonic aggregates. Information Processing and Management, 2003, Vol. 39. pp. 287–306.
  • [3] BOSC P., PIVERT O. Fuzzy queries and relational databases. In SAC ’94 Proceedings of the 1994 ACM symposium on Applied computing, 1994. pp. 170–174.
  • [4] CZOGAŁA E., Ł̨ESKI J. Fuzzy and neuro-fuzzy intelligent systems. 2000. Physica-Verlag, Springer-Verlag Comp.
  • [5] FUZZLIB. http://fuzzlib.eu. 2016.
  • [6] GRISSA-TOUZI A., HASSINE M. B. New architecture of fuzzy database management systems. The International Arab Journal of Information Technology, 2009, Vol. 6.
  • [7] HUDEC M. An approach to fuzzy database querying, analysis and realisation. Computer Science and Information Systems, 2009, Vol. 6.
  • [8] J. GALINDO, J. M. MEDINA O. P. J. C. A server for fuzzy sql queries. Lecture Notes in Computer Science, 2006, Vol. 1495. pp. 164–174.
  • [9] KUDŁACIK P. Structure of a knowledge base in the fuzzlib library (polish). Studia Informatica, 2010, Vol. 31. pp. 469–478.
  • [10] MA Z. Fuzzy database modeling with xml. 2005. Springer.
  • [11] MA Z., YAN L. Generalization of strategies for fuzzy query translation in classical relational databases. Information and Software Technology, 2007, Vol. 49. pp. 172–180.
  • [12] PONS J. M. O., VILA M. Gefred. a generalized model of fuzzy relational data bases. Information Sciences, 1994, Vol. 76. pp. 87–109.
  • [13] S. SHENOI A. M. An extended version of the fuzzy relational database model. Information Sciences, 1990, Vol. 52. p. 35–52.
  • [14] ZADEH L. A. Fuzzy sets. Information and Control, 1965, Vol. 8. pp. 338–353.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-90ea0e0a-66bc-4424-8711-f35c1cb4859f
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