Adaptive Join Algorithms in Dynamic Distributed Databases

This paper addresses the problem of query optimization fordynamic databases in distributed environments where data frequently changetheir values. An adaptive query optimization algorithm is proposed toevaluate queries. Rather than constructing a full plan for an access path andexecuting it, the algorithm constructs a partial plan, executes it, updatesthe statistics, and constructs a new partial plan. Since a partial plan isconstructed based on the latest statistics, the algorithm is adaptive to data modifications and errors from the statistics. The algorithm extends the SDD-1algorithm by considering local processing cost as well as communication cost.Whereas the SDD-1 algorithm only uses semi-joins to reduce communication cost,the algorithm reduces it with joins as well. It is proved that the adaptivealgorithm is more efficient than the SDD-1 algorithm.