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.
Highlights
It is well known that precise estimation of the future workloads can be used to eliminate many performance related problems in database systems [1]
The main objective of this paper is to propose a system of derivation rules for complex and incomplete periodic patterns and to show how such system can be used in the algorithms and in a simple prototype implementation that discovers the incomplete periodic patterns from the logs of event
This work describes a new approach to discovery of complex and incomplete periodic patterns in the logs of events
Summary
It is well known that precise estimation of the future workloads can be used to eliminate many performance related problems in database systems [1]. (2015) Discovering Complex Incomplete Periodic Patterns through Logical Derivations. An important property of periodically repeated processes says that no matter how long and how complex a process is, all its elementary operations are processed periodically. The main objective of this paper is to propose a system of derivation rules for complex and incomplete periodic patterns and to show how such system can be used in the algorithms and in a simple prototype implementation that discovers the incomplete periodic patterns from the logs of event.
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