Conferences : BDA 2012, IDEAS 2012

Abstract :
This paper describes the QOL approach to optimize distributed queries by learning. It is well-adapted to social systems (e.g. games, social networks, sharing), where data are pushed or pulled with incomplete knowledge in a dynamic environment. The contribution of this work is twofold. It first concerns the integration of the Case Based Reasoning (CBR) paradigm in query processing, providing a way to optimize queries when there is no prior knowledge on queried data sources and certainly no related metadata such as data statistics. Our approach optimizes queries using cases generated from the evaluation of similar past queries. A query case comprises: (i) the query, (ii) the query plan and (iii) the measures (computational resources consumed) of the query plan. The second aspect of the work concerns the way the CBR process interacts with the query plan generation process. This process uses classical heuristics and makes decisions randomly (e.g. when there is no statistics for join ordering and selection of algorithms, routing protocols); It also (re)uses cases (existing query plans) for similar queries parts, improving the query optimization and evaluation efficiency.