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With the rapid collection of data in a wide variety of fields - ranging from business transactions through medical investigations to scientific research - the demands on data analysis tools are ever growing. Today's challenges are less related to data storage and information retrieval, but can rather be found in the analysis of data on a global scale in a heterogeneous information system: technologies such as on-line analytical processing, data mining and knowledge discovery in databases all require the integration of information and efficient query processing. In distributed and heterogeneous datasets this can only be achieved by the efficient distribution and scheduling of subtasks in a distributed computing resource. We propose the use of mobile query optimizations based on agent-technology for distributed data warehouse and OLAP applications to adapt the concurrent query execution dynamically to the computing resource it executes on. This is of particular importance in cluster and grid computing.