Selecting topics for web resource discovery: Efficiency issues in a database approach

Abdullah Al-Hamdani*, Gultekin Ozsoyoglu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)792-802
Number of pages11
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2003
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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