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.