Query Optimization on Relational Databases for Supporting Top-k Query Processing Techniques

Information systems apply various techniques to rank query answers. Ranking queries (or top-k queries) are dominant in many emerging applications, e.g., similarity queries in multimedia databases, searching web databases, midlewares and data mining. In such application domains, end-users are more interested in the most important (top-k) query answers in the potentially huge answer space. Thus for why in relations’ join, the suitable size of relations inputs for getting top K query answers must be determined. This paper describes an algorithm for finding input size of N relations in rank aware queries to efficiently answer to the queries with join of N relations for getting top K query answers and it is observed from the experimental result that the time of query processing extraordinarily will be decreased.