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.