How to avoid building DataBlades(R) that know the value of everything and the cost of nothing

The object-relational database management system (ORDBMS) offers many potential benefits for scientific, multimedia and financial applications. However work remains in the integration of domain-specific class libraries into ORDBMS query processing. A major problem is that the standard mechanisms for query selectivity estimation, taken from relational database systems, rely on properties specific to the standard data types; creation of new mechanisms remains extremely difficult because the software interfaces provided by vendors are relatively low-level. We discuss extensions of the generalized search tree, or GiST, to support a higher level but less type-specific approach. Specifically, we discuss the computation of selectivity estimates with confidence intervals using a variety of index based approaches and present results from an experimental comparison of these methods with several estimators from the literature.

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