A (FM/DRDPE)-based approach to improve federated learning optimizer

Recently, there is a growing need for query optimization algorithm that can effectively deal with federated database systems. Modern optimizers use a cost model to choose the best query execution plan (QEP) which heavily dependent on statistics maintained in the system catalog. Keeping such statistics up to date in the federation is troublesome due to local autonomy. The main objective of this paper is to introduce a general framework for federated database system based on DB2 II to improve federated learning optimizer and enhancing global query optimization. In addition it will suggest two algorithms which may be evolved within the proposed framework to give the federation the full autonomy, precise statistics collection, efficiency in processing federated queries and permitting mid-query execution.