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Boolean query mapping across heterogeneous information sources : extended version

Author: Kevin Chen-Chuan Chang; Hector Garcia-Molina; Andreas Paepcke; Stanford University. Computer Science Department.
Publisher: Stanford, Calif. : Stanford University, Dept. of Computer Science, [1997]
Series: Report (Stanford University. Computer Science Department), no. STAN-CS-TR-97-1583.
Edition/Format:   Book : EnglishView all editions and formats
Database:WorldCat
Summary:
Abstract: "Searching over heterogeneous information sources is difficult because of the non-uniform query languages. Our approach is to allow a user to compose Boolean queries in one rich front-end language. For each user query and target source, we transform the user query into a subsuming query that can be supported by the source but that may return extra documents. The results are then processed by a filter query  Read more...
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Document Type: Book
All Authors / Contributors: Kevin Chen-Chuan Chang; Hector Garcia-Molina; Andreas Paepcke; Stanford University. Computer Science Department.
OCLC Number: 37389074
Notes: Cover title.
"This paper is an extended version of the paper entitled 'Boolean Query Mapping Across Heterogeneous Information Sources' [which] appeared in IEEE Transactions on Knowledge & Data Engineering, Vol. 8, No. 4, August 1996"--Page 1.
"January 1997."
Description: 17 pages : illustrations ; 28 cm.
Series Title: Report (Stanford University. Computer Science Department), no. STAN-CS-TR-97-1583.
Responsibility: by Kevin Chen-Chuan Chang, Hector Garcia-Molina and Andreas Paepcke.

Abstract:

Abstract: "Searching over heterogeneous information sources is difficult because of the non-uniform query languages. Our approach is to allow a user to compose Boolean queries in one rich front-end language. For each user query and target source, we transform the user query into a subsuming query that can be supported by the source but that may return extra documents. The results are then processed by a filter query to yield the correct final result. In this paper we introduce the architecture and associated algorithms for generating the supported subsuming queries and filters. We show that generated subsuming queries return a minimal number of documents; we also discuss how minimal cost filters can be obtained. We have implemented prototype versions of these algorithms and demonstrated them on heterogeneous Boolean systems."

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