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Summary: Abstract In this paper, a new technique is developed to support
the query relaxation in biological databases. Query relaxation is re-
quired due to the fact that queries tend not to be expressed exactly
by the users, especially in scientific databas s such as biological
databases, in which complex domain knowledge is heavily in-
volved. To treat this problem, we propose the concept of the so-
called fuzzy equivalence classes to capture important kinds of do-
main knowledge that is used to relax queries. This concept is fur-
ther integrated with the canonical techniques for pattern searching
such as the position tree and automaton theory. As a result, fuzzy
queries produced through relaxation can be efficiently evaluated.
This method has been successfully utilized in a practical biological
database - the GPCRDB.
Categories & Subject Decriptors: H.2.4
General Terms: Algorithms, Performance, Theory
Key Words: Query optimization, Biological databases,
Query relaxation, Position trees, Automaton
1. Introduction
Biological databases are among the most important classes
of scientific databases. A variety of biological databases
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