Abstract: A unique multi-stage classification system and method that facilitates reducing human resources or costs associated with text classification while still obtaining a desired level of accuracy is provided. The multi-stage classification system and method involve a pattern-based classifier and a machine learning classifier. The pattern-based classifier is trained on discriminative patterns as identified by humans rather than machines which allow a smaller training set to be employed. Given humans' superior abilities to reason over text, discriminative patterns can be more accurately and more readily identified by them. Unlabeled items can be initially processed by the pattern-based classifier and if no pattern match exists, then the unlabeled data can be processed by the machine learning classifier. By employing the classifiers in this manner, less human involvement is required in the classification process. Even more, classification accuracy is maintained and/or improved.

Abstract: To determine a configuration for a database system, a plurality of queries may be sampled from a representative workload using statistical inference to compute the probability of correctly selecting one of a plurality of evaluation configurations. The probability of correctly selecting may determine which and/or how many queries to sample, and/or may be compared to a target probability threshold to determine if more queries must be sampled. The configuration from the plurality of configurations with the lowest estimated cost of executing the representative workload may be determined based on the probability of selecting correctly. Estimator variance may be reduced through a stratified sampling scheme that leverages commonality, such as an average cost of execution, between queries based on query templates. The applicability of the Central Limit Theorem may be verified and used to determine which and/or how many queries to sample.

Abstract: A monitoring component of a database server collects a subset of a query workload along with related statistics. A remote index tuning component uses the workload subset and related statistics to determine a physical design that minimizes the cost of executing queries in the workload subset while ensuring that queries omitted from the subset do not degrade in performance.

Abstract: A framework is provided within a database system for specifying database monitoring rules that will be evaluated as part of the execution code path of database events being monitored. The occurrence of a selected database event triggers a rule that evaluates some parameter of an object related to the event against a condition in the rule. If the condition is met, a specified action is taken that can alter the execution of the database event or database system performance. Lightweight aggregation tables are utilized to enable aggregation of object parameter values so that presently occurring events can be compared to a summary of the object parameter values from previously occurring database events. Signatures are assigned to queries based on the structure of the query plan so that information in the lightweight aggregation tables can be grouped according to query signature.