Deriving Composite Periodic Patterns from Database Audit Trails

Information about the periodic changes of intensity and structure of database workloads plays an important role in performance tuning of functional components of database systems. Discovering the patterns in workload information such as audit trails, traces of user applications, sequences of dynamic performance views, etc. is a complex and time consuming task. This work investigates a new approach to analysis of information included in the database audit trails. In particular, it describes the transformations of information included in the audit trails into a format that can be used for discovering the periodic patterns in database workloads. It presents an algorithm thatthe fluctuations finds elementary periodic patterns through nested iterations over a four dimensional space of execution plans of SQL statements and positional parameters of the patterns. Finally, it shows the composition rules for the derivations of complex periodic patterns from the elementary and other complex patterns.