Developing cost models with qualitative variables for dynamic multidatabase environments

A major challenge for global query optimization in a multidatabase system (MDBS) is the lack of local cost information at the global level due to local autonomy. A number of methods to derive local cost models have been suggested recently. However, these methods are only suitable for a static multidatabase environment. In this paper, we propose a new multi-state query sampling method to develop local cost models for a dynamic environment. The system contention level at a dynamic local site is divided into a number of discrete contention states based on the cost of a probing query. To determine an appropriate set of contention states for a dynamic environment, two algorithms based on iterative uniform partitioning and data clustering, respectively, are introduced. A qualitative variable is used to indicate the contention states for the dynamic environment. The techniques from our previous (static) query sampling method, including query sampling, automatic variable selection, regression analysis and model validation, are extended so as to develop a cost model incorporating the qualitative variable for a dynamic environment. Experimental results demonstrate that this new multi-state query sampling method is quite promising in developing useful cost models for a dynamic multidatabase environment