Detection of resource inquiries to databases on the basis of application of self-organizing maps and fuzzy output
DOI:
https://doi.org/10.52575/2687-0932-2021-48-3-578-593Keywords:
resource-intensive database queries, self-organizing map, principal component analysis, fuzzy inference, correct query detectionAbstract
Research is focused on identifying resource-intensive requests that consume an unacceptable amount of time, processor, disks and memory resources. The tools for monitoring and optimizing queries used in modern database management systems are analyzed, their shortcomings are indicated. The urgency of the development of new intelligent tools for the timely and reliable detection of resource-intensive queries to databases has been substantiated. It is concluded that the analysis of an extended set of statistical parameters is of interest to identify resource-intensive queries. The initial set of query parameters was reduced by preliminary normalization of the set of indicators using the sigmoidal function and subsequent selection of a finite number of principal components based on the Cattell criterion. The clustering of a set of queries was performed using a self-organizing Kohonen map, in order to avoid overfitting of which an algorithm for finding the recommended radius of the topological neighborhood of active neurons was developed. To differentiate clusters, a fuzzy inference algorithm is proposed. Experimental studies have shown the feasibility of the practical use of the results obtained.
Downloads
References
Alghazali S.M.M., Polshchykov K., Hailan A.M., Svoykina L. 2021. Development of Intelligent Tools for Detecting Resource-intensive Database Queries. International Journal of Advanced Computer Science and Applications, 12(7): 32–36.
Bachhav A., Kharat V., Shelar M. 2021. An Efficient Query Optimizer with Materialized Intermediate Views in Distributed and Cloud Environment. Tehnički glasnik, 15: 105–111.
Belattar S., Abdoun O., El khatir H. 2020. New Learning Approach for Unsupervised Neural Networks Model with Application to Agriculture Field. International Journal of Advanced Computer Science and Applications, 11(5): 360–369.
Bodepudi H. 2020. Faster The Slow Running RDBMS Queries With Spark Framework. International Journal of Scientific and Research Publications, 10(11): 287–291.
Clovis L.R., Scapim C.A., Pinto R.J.B. et al. 2020. Yield stability analysis of maize hybrids using the self-organizing map of Kohonen. Euphytica, 216: 161.
Fattah S.M.A., Mahmoud M.A., Abd-Elmegid L.A.E. 2014. An Adaptive Hybrid Controller for DBMS Performance Tuning. International Journal of Advanced Computer Science and Applications,
(5): 151–156.
Fernandez I. 2015. Beginning Oracle Database 12c Administration. From Novice to Professional. Apress: 384.
Kalmegh P., Babu S., Roy S. 2018. Analyzing Query Performance and Attributing Blame for Contentions in a Cluster Computing Framework. arXiv:1708.08435v2.
Lan H., Bao Z., Peng Y.A. 2021. Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration. Data Sci. Eng., 6: 86–101.
Lekshmi B.G., Meyer-Wegener K. 2021. COPRAO: A Capability Aware Query Optimizer for Reconfigurable Near Data Processors. 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW): 54-59.
Ma M., Yin Z., Zhang S. et al. 2020. Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases. PVLDB, 13(8): 1176–1189.
Miao Z., Chen T., Bendeck A. et al. 2020. I-Rex: an interactive relational query explainer for SQL. Proc. VLDB Endow, 13: 2997–3000.
Muniswamaiah M., Agerwala T., Tappert C.C. 2020. Approximate Query Processing for Big Data in Heterogeneous Databases. 2020 IEEE International Conference on Big Data: 5765–5767.
Polshchykov K.A., Lazarev S.A., Konstantinov I.S., Polshchykova O.N., Svoikina L.F., Igityan E.V., Balakshin M.S. 2020. Assessing the Efficiency of Robot Communication. Russian Engineering Research, 40: 936–938.
Polshchykov K., Lazarev S., Polshchykova O., Igityan E. 2019. The Algorithm for Decision-Making Supporting on the Selection of Processing Means for Big Arrays of Natural Language Data. Lobachevskii Journal of Mathematics, 40 (11): 1831–1836.
Polshchykov K.O., Lazarev S.A., Zdorovtsov A.D. 2017. Neuro-Fuzzy Control of Data Sending in a Mobile Ad Hoc Network. Journal of Fundamental and Applied Sciences, 9(2S): 1494–1501.
Polshchykov K.O., Zdorenko Y.M., Masesov M.O. 2014. Method of telecommunications channel throughput distribution based on linear programming and neuro fuzzy predicting. Elixir International Journal. Network Engineering, 75: 27327–27334.
Sakkari M., Zaied M. 2020. A convolutional deep self-organizing map feature extraction for machine learning. Multimedia Tools and Applications, 79: 19451–19470.
Sikdar S., Jermaine C. 2020. MONSOON: Multi-Step Optimization and Execution of Queries with Partially Obscured Predicates. Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD '20): 225–240.
Sinha S., Singh T.N., Singh V.K., Verma A.K. 2010. Epoch determination for neural network by self-organized map (SOM). Computational Geosciences, 14: 199–206.
Wang C., Cheungc A., Bodik R. 2017. Synthesizing highly expressive SQL queries from input-output examples. Proceedings of the 38th ACM SIGPLAN Conf. on Programming Language Design and Implementation: 452–466.
Zhou X., Ordonez C. 2020. Matrix Multiplication with SQL Queries for Graph Analytics. 2020 IEEE International Conference on Big Data (Big Data): 5872–5873.
Abstract views: 141
Share
Published
How to Cite
Issue
Section
Copyright (c) 2021 ECONOMICS. INFORMATION TECHNOLOGIES
This work is licensed under a Creative Commons Attribution 4.0 International License.
