Relaxing Queries with Hierarchical Quantified Data Abstraction

Abstract

Query relaxation is one of the crucial components for approximate query answering. Query relaxation has extensively been investigated in terms of categorical data; few studies, however, have been effectively established for both numerical and categorical data. In this article, we develop a query relaxation method by exploiting hierarchical quantified data abstraction, and a novel method is proposed to quantify the semantic distances between the categorical data so that the query conditions for categorical data are effectively relaxed. We additionally introduce query relaxation algorithms to modify the approximate queries into ordinary queries, which are followed by a series of examples to represent the modification process. Our method outperformed the conventional approaches for the various combinations of complex queries with respect to the cost model and the number of child nodes.

An Integrated Query Relaxation Approach Adopting Data Abstraction and Fuzzy Relation

“…The knowledge abstraction hierarchy (KAH) (Huh & Lee, 2001;Huh & Moon, 2000;Shin et al, 2008) extended previous abstraction hierarchy approaches by capturing not only value abstraction but also domain abstraction knowledge from underlying databases. KAH can support more effective query processing by increasing the diversity of admitted queries and by accommodating dynamic abstraction knowledge maintenance.…”

“…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 DOI: 10.4018/jdm.2010100103 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 & Chen, 1994;Liu & Chu, 2007;Cuppens & Demolombe, 1989;Cuzzocrea, 2005Cuzzocrea, , 2007De Sean & Furtado, 1998;Godfrey, 1997;Huh & Lee, 2001;Huh & Moon, 2000;Hung, Wermter, & Smith, 2004;Marshall, Chen & Madhusudan, 2005;Mao & Chu, 2007;Motro, 1988Motro, , 1990Minker, 1998;Shin, Huh, Park, & Lee, 2008).…”

“…To provide a wider range of approximate answers by relaxing search conditions, cooperative query answering requires a human expert's knowledge of the underlying database semantics (e.g., similarity strength between data values). A variety of knowledge representation frameworks have been researched, including the abstraction hierarchy (Cai, Cercone, & Han, 1993;Liu & Chu, 2005;Chu, Yang, Chiang, et al, 1996;Liu & Chu, 2007;Huh & Lee, 2001;Huh & Moon, 2000;Shin et al, 2008), the semantic distance (Motro, 1988(Motro, , 1990, and the logic model (De Sean & Furtado, 1998;Godfrey, 1997). However, each framework is limited for effective cooperative query answering with respect to the following requirements:…”

A Cost-Based Range Estimation for Mapping Top-k Selection Queries over Relational Databases

“…languages, users face the challenge of routinely specifying value ranges of attributes in search of approximate results, particularly in the event of empty answers or too many answers (Huh & Lee, 2001;Shin, Huh, Park & Lee, 2008). Top-k querying aims to address this problem, and is defined in the recent literature as a method of specifying relational queries by target values of attributes in order to obtain a desired number of best matches based on some ranking (distance) functions (Bruno, Chaudhuri & Gravano, 2002;Chaudhuri & Gravano, 1999).…”