Selecting Topics for Web Resource Discovery: Efficiency Issues in a Database Approach

This paper discusses algorithms for topic selection queries, designed to query a database containing metadata about web information resources. The metadata database contains topics and relationships, called metalinks, about topics. Topics in the database contain associated importance scores. The top...

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Bibliographic Details
Main Authors: Al-Hamdani, Abdullah, Ozsoyoglu, Gultekin
Format: Book Chapter
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Disjunctive Normal Form
Exact sciences and technology
False Dismissal
Importance Score
Information systems. Data bases
Memory organisation. Data processing
Query Evaluation
Software
Topic Selection
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Summary:This paper discusses algorithms for topic selection queries, designed to query a database containing metadata about web information resources. The metadata database contains topics and relationships, called metalinks, about topics. Topics in the database contain associated importance scores. The topic selection operator TSelection selects, within time T, topics that satisfy a given selection formula and having output importance scores above a given threshold value or in the top-k. The selection formula contains expensive predicates, in the form of user-defined functions. To minimize the number of expensive predicate evaluations (probes) in the TSelection algorithm, we introduce and evaluate three heuristics. Also, due to the time constraint T, the TSelection algorithm may terminate without locating all output tuples. In order to maximize the number of output tuples found, we introduce and evaluate three heuristics to locate a tuple to evaluate at a given time.
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-45227-0_77