Abstract: A window size for outlier detection in a time series of a database system is determined. Strength values are calculated for data points using a set of window sizes, resulting at least in one set of strength values for each window size. The strength values increase as a distance between a value of a respective data point and a local mean value increases. For each set of strength values, a weighted sum is calculated based on the respective set of strength values. A weighting function is used to suppress the effect of largest strength values and a window size is selected based on the weighted sums.

Abstract: The invention finds matches in ordered sequences, e.g. program function call stacks from anomalous software program executions for discovering related or identical software flaws and possible known solutions. Call stacks are matched after removing recursive and uninformative subpatterns, e.g. names of common error handling routines, to see if they were generated due to the same possibly known problem. Sequences statistically unlikely to be matches are pruned from the search. Matches found earlier in the sequences may be weighted differently, e.g. to be more important in call stacks, than other matches. Sequences are indexed so that those relevant to a query are identified automatically without detailed exhaustive search. Maximum matches are found by optimizing a cost function based on weighted sequence similarity.

Abstract: A method for scheduling a workload comprising a plurality of transactions relating to a database management system is disclosed. The method comprises the steps of determining a log space amount required by each transaction in the workload, estimating the total log space required for executing the workload based on the log space amounts required by each transaction, and scheduling execution of the workload based on the estimated total log space.

Abstract: A method for scheduling a workload comprising a plurality of transactions relating to a database management system is disclosed. The method comprises the steps of determining a log space amount required by each transaction in the workload, estimating the total log space required for executing the workload based on the log space amounts required by each transaction, and scheduling execution of the workload based on the estimated total log space.

Abstract: A window size for outlier detection in a time series of a database system is determined. Strength values are calculated for data points using a set of window sizes, resulting at least in one set of strength values for each window size. The strength values increase as a distance between a value of a respective data point and a local mean value increases. For each set of strength values, a weighted sum is calculated based on the respective set of strength values. A weighting function is used to suppress the effect of largest strength values and a window size is selected based on the weighted sums.

Abstract: A method for scheduling a workload comprising a plurality of transactions relating to a database management system is disclosed. The method comprises the steps of determining a log space amount required by each transaction in the workload, estimating the total log space required for executing the workload based on the log space amounts required by each transaction, and scheduling execution of the workload based on the estimated total log space.

Abstract: A method and system to predict future performance of a database management system (DBMS) is disclosed. The invention uses a time series of historical data of operating parameters to predict future values of the operating parameters. The predicted future values of the operating are used to predict the future performance of the DBMS.

Abstract: The invention finds matches in ordered sequences, e.g. program function call stacks from anomalous software program executions for discovering related or identical software flaws and possible known solutions. Call stacks are matched after removing recursive and uninformative subpatterns, e.g. names of common error handling routines, to see if they were generated due to the same possibly known problem. Sequences statistically unlikely to be matches are pruned from the search. Matches found earlier in the sequences may be weighted differently, e.g. to be more important in call stacks, than other matches. Sequences are indexed so that those relevant to a query are identified automatically without detailed exhaustive search. Maximum matches are found by optimizing a cost function based on weighted sequence similarity.