الفهرس 
Introduction and Review of Earlier WorkOnly 14 pages are availabe for public view
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Abstract
A distributed database is a collection of logically related databases that cooperate in a transparent manner.Query processing uses a communication network for transmitting of data between sites. Query optimization represents one of the hardest challenges in the database area. The development of sophisticated query optimization technology is the reason for the great commercial success of database systems which complexity and cost increases with increasing number of relations in the query. The basic components of the distributed query optimizer are search space,search strategy, and cost model. Cost is the sum of local host(I/O cost and CPU cost at each site)and the cost of transferring data between sites. Numerous search strategies like static,deterministic and randomized strategies are available for determining an optimal plan. However, these strategies are not suitable for the autonomous distributed database systems.Mariposa, query trading (QT),query trading with processing task trading (QTPT) and K-QTPT strategies were developed for the autonomous distributed database systems, but they incur high optimization cost because of involvement of all nodes in generating optimal plan. Then an enhanced strategy was proposed for reducing the high optimization cost that incurred by K-QTPT. This proposed strategy gradually sets priorities for only k winner nodes then sends RFBs for the high priorities winner nodes for quick responses.Keywords: distributed database systems , distributed queries , query optimization ,autonomous strategies