A Heuristic Query Optimization Approach for Heterogeneous Environments

In a rapidly growing digital world the ability to discover, query and access data efficiently is one of the major challenges we are struggling today. Google has done a tremendous job by enabling casual users to easily and efficiently search for Web documents of interest. However, a comparable mechanism to query data stocks located in distributed databases is not available yet. Therefore our research focuses on the query optimization of distributed database queries, considering a huge variety on different infrastructures and algorithms. This paper introduces a novel heuristic query optimization approach based on a multi-layered blackboard mechanism. Moreover, a short evaluation scenario proofs our investigations that even small changes in the structure of a query execution tree (QET) can lead to significant performance improvements.