Multi-query Optimization in Federated Databases Using Evolutionary Algorithm

Multi Query Optimization in federated database systems is a well-studied area. Studies have shown that similar problem arises in wide range of applications, e.g., distributed stream processing systems and wireless sensor networks. In this paper, a general distributed multiquery processing problem motivated by the need to speedup data acquisition in federated databases using evolutionary algorithm is studied. We setup a simple framework in which each individual in population is evolved in terms of cost, uniform labeling of hyper edges and validity of resource constraints through a number of generations. Variations of our general problem can be shown to be NP-Hard. Our extensive empirical evaluation over five different synthetic datasets shows a significant improvement of 8 percent in results as compared to the state-of-the-art methods.