About Top-k Flexible Queries in Large Databases

The problem of obtaining efficient answers to topk queries has attracted a lot of research attention. 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 a new approach for top-k flexible queries taking into account another degree of granularity in the process of the evaluation of the query. We start by generating a MetaDB formed by a set of clusters resulting of a preliminary fuzzy classification on the data. This set represents a reduced view of the initial DB and permits to deduct the semantics of the initial DB. We prove that our approach permits an optimal search of the relevant data sources and generate automatically the better k answers while proposing a new operator called stratified operator for taking into account the user’s preferences. Keywords-Large databases; flexible query; preference; formal concept analysis; Top k.