Affiliations: [a] Université de Tunis El Manar, ENIT, FST, LIPAH, Tunisia | [b] Université de Tunis El Manar, FST, Tunisia
Correspondence:
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Corresponding author: Amel Grissa Touzi, Université de Tunis El Manar, ENIT, FST, LIPAH, Tunisia. E-mail:[email protected]
Abstract: Several algorithms of the top-k retrieval problem have
been introduced in recent years. Unfortunately, current top-k query
processing techniques focus on Boolean queries, and cannot be applied to the
large Data Bases (DB) seen the gigantic number of data. In this paper, we
propose an intelligent approach for top-k flexible queries taking into
account another degree of granularity in the process of the evaluation of
the query. It consists of two steps: 1) Generate a Meta-DB formed by a set
of clusters resulting of a preliminary fuzzy clustering on the data. This
set represents a reduced view of the initial DB and permits to deduct the
semantics of the initial DB, and 2) Generate automatically the top k results
using this Meta-DB and a new operator called stratified
operator. We prove that this approach is optimum sight that the
evaluation of the query is not done on the set of starting data which are
enormous but rather by using the Meta-DB. Moreover, this approach can be
applied to the large DB and that it does not require modifying the SQL
language.
