Answering Conjunctive Fuzzy Query Using Views: Efficient Algorithm to Return the Best k-Tuples: Middleware Based Context-Aware Pervasive Healthcare Vase Study

We proposed in a previous work an algorithm to compute the degree of satisfaction of a given conjunctive fuzzy query rewriting based on the histograms approximating, in the query result, the frequency distribution of values in the attributes to which fuzzy conditions are related. A simple way to answer a conjunctive fuzzy query is to apply one of the existing algorithms for answering classical queries using views in order to determine all the possible rewritings. Then, to compute the satisfaction degree for each rewriting and to keep and run only those with accepted scores. However, this method incurs an unnecessary cost since it determines also the useless query rewritings and waste of resources for useless scores computation. To overcome these drawbacks, we propose in this paper an efficient algorithm to generate the N best query rewritings which are likely to return the top k-tuples fitting with user preferences. The proposed algorithm discards useless rewritings from the query rewritings searching step and hence, generates the likely satisfactory ones ordered according to their satisfaction degrees of the user query. This method accelerates query processing and reduces query running time. We show in this paper the interest of this algorithm in middleware applications by using a case study in middle ware based context-aware pervasive health care where doctors, haven't enough time to fix carefully their queries in some emergency cases, require to flexibly use medical applications and need to quickly receiving best answers while treating patients in the shortest waiting time and to free resources for eventually other urgent requests.