An Integrated Query Relaxation Approach Adopting Data Abstraction and Fuzzy Relation

An Integrated Query Relaxation Approach Adopting Data Abstraction and Fuzzy Relation

Soon-Young Huh (Korea Advanced Institute of Science and Technology, Korea), Kae-Hyun Moon (Samsung Electronics Co., Korea) and Jinsoo Park (Seoul National University, Korea)
Copyright: © 2010 |Pages: 25
DOI: 10.4018/jdm.2010100103
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Abstract

This paper proposes a cooperative query answering approach that relaxes query conditions to provide approximate answers by utilizing similarity relationships between data values. The proposed fuzzy abstraction hierarchy (FAH) represents a similarity relationship based on the integrated notion of data abstraction and fuzzy relations. Based on FAH, the authors develop query relaxation operators like query generalization, approximation, and specialization of a value. Compared with existing approaches, FAH supports more effective information retrieval by processing various kinds of cooperative queries through elaborate relaxation control and providing ranked query results according to fitness scores. Moreover, FAH reduces maintenance cost by decreasing the number of similarity relationships to be managed.
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1. Introduction

Query processing based on conventional database systems often fails to provide the information users really want if the user does not provide a precise query statement. Database systems may return null responses when the exact answers to queries do not exist. Conversely, the non-empty responses implying a qualified data set to queries may not satisfy the user who wants not only exact answers but also additional approximate answers. Furthermore, the schema and semantics of databases are often too complex for ordinary users to understand in their entirety to compose intended queries.

If a query processing system understands the schema and semantics of the database, it will be able to return informative responses beyond a query’s requested answer set and greatly help the user obtain relevant answers in various decision support application systems. To support such intelligent query processing, a number of cooperative query answering approaches have been introduced, which provide a human-oriented interface to a database system by facilitating the relaxation of query conditions to produce approximate answers. Typically, cooperative query answering analyzes the intent of a query and transforms the query into a new query of greater scope by relaxing the original query conditions (Liu & Chu, 1993; Chu, Yang, Chiang, Minock, Chow, & Larson, 1996; Chu, Yang, & Chow, 1996; Chu & Chen, 1994; Liu & Chu, 2007; Cuppens & Demolombe, 1989; Cuzzocrea, 2005, 2007; De Sean & Furtado, 1998; Godfrey, 1997; Huh & Lee, 2001; Huh & Moon, 2000; Hung, Wermter, & Smith, 2004; Marshall, Chen & Madhusudan, 2005; Mao & Chu, 2007; Motro, 1988, 1990; Minker, 1998; Shin, Huh, Park, & Lee, 2008).

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