Method and system for real-time, load-driven multidimensional and hierarchical classification of monitored transaction executions for visualization and analysis tasks like statistical anomaly detection

Abstract

A system and method is disclosed that analyzes a set of historic transaction traces to identify an optimized set of transaction clusters with the highest transaction frequency. The transaction clusters are defined according to multiple parameters describing the execution context of the analyzed transactions. The transaction clusters are described by coordinates in a multidimensional, hierarchical classification space. Descriptive statistical data is extracted from historic transactions corresponding to previously identified transaction clusters and stored as reference data. Transaction trace data from currently executed transactions is analyzed to find a best matching historic transaction cluster. The current transaction traces are grouped according to their corresponding historic transaction cluster. Statistical data is extracted from those groups of current transaction trace and statistical test are performed that compare current and historic data on a per historic transaction cluster basis to identify deviations in performance and functional behavior of current and historic transactions.