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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 644266260
doi: 10.4316/AECE


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  3/2015 - 5

Application of Machine Learning Algorithms for the Query Performance Prediction

MILICEVIC, M. See more information about MILICEVIC, M. on SCOPUS See more information about MILICEVIC, M. on IEEExplore See more information about MILICEVIC, M. on Web of Science, BARANOVIC, M. See more information about  BARANOVIC, M. on SCOPUS See more information about  BARANOVIC, M. on SCOPUS See more information about BARANOVIC, M. on Web of Science, ZUBRINIC, K. See more information about ZUBRINIC, K. on SCOPUS See more information about ZUBRINIC, K. on SCOPUS See more information about ZUBRINIC, K. on Web of Science
 
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Download PDF pdficon (1,442 KB) | Citation | Downloads: 272 | Views: 725

Author keywords
machine learning, prediction algorithms, query processing, transaction databases

References keywords
learning(16), performance(13), data(13), prediction(12), machine(12), database(12), systems(10), query(10), workloads(7), francisco(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-08-31
Volume 15, Issue 3, Year 2015, On page(s): 33 - 44
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.03005
Web of Science Accession Number: 000360171500005
SCOPUS ID: 84940732050

Abstract
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This paper analyzes the relationship between the system load/throughput and the query response time in a real Online transaction processing (OLTP) system environment. Although OLTP systems are characterized by short transactions, which normally entail high availability and consistent short response times, the need for operational reporting may jeopardize these objectives. We suggest a new approach to performance prediction for concurrent database workloads, based on the system state vector which consists of 36 attributes. There is no bias to the importance of certain attributes, but the machine learning methods are used to determine which attributes better describe the behavior of the particular database server and how to model that system. During the learning phase, the system's profile is created using multiple reference queries, which are selected to represent frequent business processes. The possibility of the accurate response time prediction may be a foundation for automated decision-making for database (DB) query scheduling. Possible applications of the proposed method include adaptive resource allocation, quality of service (QoS) management or real-time dynamic query scheduling (e.g. estimation of the optimal moment for a complex query execution).


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References Weight

Web of Science« Citations for all references: 342 TCR
SCOPUS« Citations for all references: 2,734 TCR

Web of Science« Average Citations per reference: 7 ACR
SCOPUS« Average Citations per reference: 54 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2016-12-02 17:11 in 203 seconds.




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