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FACTS & FIGURES

JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: May 2018
Next issue: Aug 2018
Avg review time: 107 days


PUBLISHER

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: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  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
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,442 KB) | Citation | Downloads: 372 | Views: 1,471

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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Copyright ©2001-2018
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

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