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, and sequences of dynamic performance views, 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 the fluctuations of database workloads. It presents an algorithm that finds elementary periodic patterns through nested iterations over a four-dimensional space of execution plans of SQL statements and positional parameters of the patterns. It proposes a collection of composition rules for the derivations of complex periodic patterns from the elementary and other complex patterns and it shows how to use such rules to predict the future workload levels.
Information about periodic processing of database operations has a pivotal importance for continuous physical database design and automated performance tuning of database systems. This work shows how to detect the oscillations of database workloads caused by the periodical invocations of user applications. In particular, we present an algorithm for discovering periodic patterns in the histories of processing of complex and elementary database operations. In our approach, information collected from the database audit trails is transformed into a sequence of syntax trees and later on it is compressed in a syntax tree
Abstract2014 IEEE. Information about periodic computations of processes, events, and software components can be used to improve performance of software systems. This work investigates mining periodic patterns of events from historical information related to processes, events, and software components. We introduce a concept of a nested event log that generalizes historical information stored in the application traces, event logs and dynamic profiles. We show how a nested event log can be compressed into a reduced event table and later on converted into a workload histogram suitable for mining periodic patterns of events. The paper defines a concept of periodic pattern and its validation in a workload histogram. We propose two algorithms for mining periodic patterns and we define the quality indicators for the patterns found. We show, that a system of operations on periodic patterns introduced in this work can be used to derive new periodic patterns with some of the quality indicators better from the original ones. The paper is concluded with an algorithm for deriving periodic patterns with the given quality constraints. Abstract-Information about periodic computations of processes, events, and software components can be used to improve performance of software systems. This work investigates mining periodic patterns of events from historical information related to processes, events, and software components. We introduce a concept of a nested event log that generalizes historical information stored in the application traces, event logs and dynamic profiles. We show how a nested event log can be compressed into a reduced event table and later on converted into a workload histogram suitable for mining periodic patterns of events. The paper defines a concept of periodic pattern and its validation in a workload histogram. We propose two algorithms for mining periodic patterns and we define the quality indicators for the patterns found. We show, that a system of operations on periodic patterns introduced in this work can be used to derive new periodic patterns with some of the quality indicators better from the original ones. The paper is concluded with an algorithm for deriving periodic patterns with the given quality constraints.
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