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Bipolar Queries: A Way to Deal with Mandatory and Optional Conditions in Database Querying

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Uncertainty Approaches for Spatial Data Modeling and Processing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 271))

Abstract

Abstract.We discuss an approach to bipolar queries.We start with the original idea proposed in Lacroix and Lavency and review some selected relevant approaches recently proposed in the literature. In particular we point out two main lines of research, the one focusing on a formal representation within some well established theories and the analysis of a meaningful combinations of multiple conditions, and the one concerned mainly with the study of how to aggregate mandatory (negative, or required) and optional (positive, or desired) conditions. We follow the second line of reasoning and show some relations with other approaches, both concerning database querying, exemplified by Chomicki’s queries with preferences, and Yager’s works in multicriteria decision making. In the former case we offer a fuzzy counterpart of a new relational algebra operator winnow and show how a bipolar query can be represented via the select and winnow operators.

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Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warszawa, Poland

    Sławomir Zadroz̈ny & Janusz Kacprzyk

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  1. Sławomir Zadroz̈ny
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  2. Janusz Kacprzyk
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Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Ul.Newelska 6, 01-447, Warszawa, Poland

    Janusz Kacprzyk

  2. Computer Scientist, Section 7440.5, Naval Research Laboratory, Stennis Space Center, 39529, MS, USA

    Frederick E. Petry

  3. Multimedia Database Laboratory, Department of Computer Engineering, Middle East Technical University, 06531, Ankara, Turkey

    Adnan Yazici

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Zadroz̈ny, S., Kacprzyk, J. (2010). Bipolar Queries: A Way to Deal with Mandatory and Optional Conditions in Database Querying. In: Kacprzyk, J., Petry, F.E., Yazici, A. (eds) Uncertainty Approaches for Spatial Data Modeling and Processing. Studies in Computational Intelligence, vol 271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10663-7_8

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  • DOI: https://doi.org/10.1007/978-3-642-10663-7_8

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