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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: 643243560
doi: 10.4316/AECE


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2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
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2021-Jun-06
SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2020 is 2.5, better than all our previous results.

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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: 845 | Views: 576

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).


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 2
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Cited-By CrossRef

[1] Autonomic performance prediction framework for data warehouse queries using lazy learning approach, Raza, Basit, Aslam, Adeel, Sher, Asma, Malik, Ahmad Kamran, Faheem, Muhammad, Applied Soft Computing, ISSN 1568-4946, Issue , 2020.
Digital Object Identifier: 10.1016/j.asoc.2020.106216
[CrossRef]

[2] Autonomic workload performance tuning in large-scale data repositories, Raza, Basit, Sher, Asma, Afzal, Sana, Malik, Ahmad Kamran, Anjum, Adeel, Kumar, Yogan Jaya, Faheem, Muhammad, Knowledge and Information Systems, ISSN 0219-1377, Issue 1, Volume 61, 2019.
Digital Object Identifier: 10.1007/s10115-018-1272-0
[CrossRef]

[3] Tifinagh Handwriting Character Recognition Using a CNN Provided as a Web Service, Kadri, Ouahab, Benyahia, Abderrezak, Abdelhadi, Adel, International Journal of Cloud Applications and Computing, ISSN 2156-1834, Issue 1, Volume 12, 2022.
Digital Object Identifier: 10.4018/IJCAC.297093
[CrossRef]

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