Discovering Complex Incomplete Periodic Patterns through Logi-cal Derivations

Abstract Discovering complex and incomplete periodic patterns in the logs of events is a complicated and time consuming task. This work shows that it is possible to discover complex and incomplete periodic patterns through finding simple patterns first and through logical derivations of complex and incomplete patterns later on. The paper defines a syntax and semantics of a class of periodic patterns that frequently occur in the logs of events. A system of derivation rules proposed in the paper can be used to transform a set of periodic patterns into a logically equivalent set of patterns. The rules are used in the algorithms that derive complex and incomplete periodic patterns. A prototype implementation of the algorithms that discover complex and incomplete periodic patterns in the logs of events is presented

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