Mining Periodic Workload Patterns in Database Audit Trails

Zimniak, Martin; Getta, Janusz R.; Benn, Wolfgang

doi:10.14257/ijdta.2013.6.6.06

Cited by 2 publications

(6 citation statements)

References 15 publications

(12 reference statements)

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“…To prepare a database system for the workload peaks it is possible to get more information on the periodicity of database applications and implementation details of database operations from the logs, traces, audit trails, etc., [2]. In particular, we need information about the sequences and frequencies of elementary database operations executed during the computations of queries and data manipulation statements.…”

Section: Proceedings Of 2016 6th International Workhop On Computer Smentioning

confidence: 99%

“…The model of periodicity considered in this paper is an extension of the model introduced in [2] for the analysis of database audit trails. In this work we consider a class periodic patterns defined in [14] with periodic pattern discovery algorithms described in [15].…”

Section: Previous Workmentioning

confidence: 99%

“…As an example consider a set of periodic patterns {p 1 , p 2 , p 3 } such that p 1 started in a time unit H [1], p 2 started in a time unit H [3], and p 3 started in a time units H [4]. Next, assume that an anchor event e 1 has happened in a time unit H [1], an event e 2 has happened in H [2] and an event e 3 has happened in H [4]. Therefore, we obtain the following history of anchor events {<e 1 ,H [1]>, <e 2 ,H [2]>, <e 3 ,H [4]>}.…”

Section: Static Workload Estimationmentioning

confidence: 99%

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Using Periodic Patterns for Dynamic Estimation of Future Database Workloads

Getta¹,

Zimniak²

2016

Proceedings of 2016 the 6th International Workshop on Computer Science and Engineering

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Analysis of historical database workloads can be used to improve the future performance of database systems. This work investigates a problem how to use information about periodic processing of database applications recorded in the past for static and dynamic estimation of the future database workloads. The new concepts of anchor events and event expressions are used to create the mappings of past periodic patterns in data processing into the future database workloads. We show that event expressions allow for both static and dynamic estimation of future database workloads. The paper presents the algorithms for static a dynamic estimation of future database workloads from information about time distributions of periodic processing of database applications and information about the events that trigger processing of the applications.

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Section: Proceedings Of 2016 6th International Workhop On Computer Smentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Static Workload Estimationmentioning

confidence: 99%

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Using Periodic Patterns for Dynamic Estimation of Future Database Workloads

Getta¹,

Zimniak²

2016

Proceedings of 2016 the 6th International Workshop on Computer Science and Engineering

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“…The works [28] and [29] review a class of data mining techniques based on analysis of ordered set of operations on data performed by the user applications. The model of periodicity considered in this paper is an extension of the model introduced in [30].…”

Section: Previous Workmentioning

confidence: 99%

“…A model of workload histograms described in this section is based on the model developed earlier for audit trails in database systems [30]. We consider a period of time t start , t end over which a log event and respective dynamic profile are recorded.…”

Section: Workload Histogramsmentioning

confidence: 99%

Mining Periodic Patterns from Nested Event Logs

Getta

Zimniak

Benn

2014

2014 IEEE International Conference on Computer and Information Technology

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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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Mining Periodic Workload Patterns in Database Audit Trails

Cited by 2 publications

References 15 publications

Using Periodic Patterns for Dynamic Estimation of Future Database Workloads

Using Periodic Patterns for Dynamic Estimation of Future Database Workloads

Mining Periodic Patterns from Nested Event Logs

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