RELATIONAL DATABASE WORK OPTIMIZATION OF THE TRANSPORT INFORMATION SYSTEM

Authors

DOI:

https://doi.org/10.32703/2617-9040-2022-40-23

Keywords:

relational databases, information systems, query optimization, database management systems, SQL.

Abstract

Informatization of society poses new challenges and requirements for information systems in transport, which are responsible for data processing and storage. A significant number of performance problems in developed applications are related to databases, because often poor database performance directly affects the application's slowdown. For the normal functioning of the processes of diagnostics of rolling stock and automation systems, decision-making, and the formation of reports in the transport industry, the task of optimizing the management of databases and ensuring their productivity under the condition of simultaneous multi-user access arises. The article discusses the stages of query execution and approaches to increasing the efficiency of the query optimizer. The main factors that depend on the performance of queries in information systems based on relational databases are analyzed. Optimization criteria can include such performance estimates as time required to generate a report, query execution time, speed of finding data in non-indexed fields, maximum number of simultaneous accesses to data in multi-user mode, speed of indexing, as well as update, delete and add operations.

References

Shah, B., Jat, P. M., & Sashidhar, K. (2022). Performance Study of Time Series Databases. arXiv preprint arXiv:2208.13982. https://doi.org/10.48550/arXiv.2208.13982.

Mostafa, J., Wehbi, S., Chilingaryan, S., & Kopmann, A. (2022, July) SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things. 34th International Conference on Scientific and Statistical Database Management (SSDBM 2022). (6–8 July, 2022). (pp. 1-11). Copenhagen, Denmark. ACM, New York, NY, USA. https://doi.org/10.1145/3538712.3538723.

Chebotko A., Kashlev A., & Lu, S. (2015, June). A big data modeling methodology for Apache Cassandra. In 2015 IEEE International Congress on Big Data. (pp. 238-245). IEEE., New York, NY, USA, 2015. https://doi.org/10.1109/BigDataCongress.2015.41

Sivasubramanian, S. (2012, May). Amazon dynamoDB: a seamlessly scalable non-relational database service. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. (pp. 729-730). https://doi.org/10.1145/2213836.2213945.

Alsubaiee, S., Altowim, Y., Altwaijry, H., Behm, A., Borkar, V., Bu, Y., Carey, M., Cetindil, I., Cheelangi, M., Faraaz, K., Gabrielova, E., Grover, R., Heilbron, Z., Kim, Y.-S., Li, C., Li, G., Ok, J. M., Onose, N., Pirzadeh, P., Tsotras, V., Vernica, R., Wen, J., & Westmann, T. (2014). AsterixDB: A Scalable,Open Source BDMS. Proc. VLDB Endow, 1905–1916. https://doi.org/10.14778/2733085.2733096.

Chickerur, S., Goudar, A., & Kinnerkar, A. (2015, November). Comparison of relational database with documentoriented database (mongodb) for big data applications. In 2015 8th International Conference on Advanced Software Engineering & Its Applications (ASEA). (pp. 41-47). IEEE. https://doi.org/10.1109/ASEA.2015.19

Jung, M. G., Youn, S. A., Bae, J., & Choi, Y. L. (2015, November). A study on data input and output performance comparison of mongodb and postgresql in the big data environment. In 2015 8th international conference on database theory and application (DTA). (pp. 14-17). IEEE. https://doi.org/10.1109/DTA.2015.14.

Kang, Y. S., Park, I. H., Rhee, J., & Lee, Y. H. (2015). MongoDB-based repository design for IoT-generated RFID/sensor big data. IEEE Sensors Journal, 16(2), 485-497. https://doi.org/10.1109/JSEN.2015.2483499

Imasheva, B., Azamat, N., Sidelkovskiy, A., & Sidelkovskaya, A. (2019, July). The practice of moving to big data on the case of the nosql database, clickhouse. In World Congress on Global Optimization (pp. 820-828). Springer, Cham. https://doi.org/10.1007/978-3-030-21803-4_113.

Борейко О. Ю. (2018) Rozroblennia bazy danykh dlia avtomatyzovanoi systemy opratsiuvannia parametriv pasazhyropotokiv hromadskoho transportu [Modeling and information technologies]. Modeliuvannia ta informatsiini tekhnolohii - Modeling and information technologies, 82, 177-184. http://nbuv.gov.ua/UJRN/Mtit_2018_82_26.

Jiang, T., Wu, Z., Song, Y., Liu, X., Liu, H., & Zhang, H. (2013). Sustainable transport data collection and application: china urban transport database. Mathematical Problems in Engineering, 2013, 1-10. https://doi.org/10.1155/2013/879752

Liu, M., Fang, S., Dong, H., & Xu, C. (2021). Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems, 58, 346-361. https://doi.org/10.1016/j.jmsy.2020.06.017.

Саяпіна І.О., Горобченко О.М., Демченко В.О., Штомпель Ю.М. (2022) Modeliuvannia ta intelektualnyi analiz parametriv tonalnoho reikovoho kola [Modeling and intellectual analysis of the parameters of the tonal rail circuit]. // Zbirnyk naukovykh prats DUIT seriia «Transportni systemy i tekhnolohii». K.: DUIT - Collection of scientific works DUIT series «Transport systems and technologies». K.: DUIT, 2022, 39, 167-174 [in Ukrainian]. https://doi.org/10.32703/2617-2022-39-16.

Саяпіна І.О. (2022) Zastosuvannia metodu klasyfikatsii danykh ta neironnykh merezh dlia pidvyshchennia zavadostiikosti reikovoho kola [Application of the method of data classification and neural networks to increase the interference resistance of the rail circuit] // Zbirnyk naukovykh prats DUIT seriia «Transportni systemy i tekhnolohii». K.: DUIT - Collection of scientific works DUIT series «Transport systems and technologies». K.: DUIT, 2022, 39, 266-277 [in Ukrainian]. https://doi.org/10.32703/2617-9040-2022-39-26.

Samson, S., & Aponso, A. (2020, May). An Analysis on Automatic Performance Optimization in Database Management Systems. In 2020 World Conference on Computing and Communication Technologies (WCCCT) (pp. 6-9). IEEE. https://doi.org/10.1109/WCCCT49810.2020.9169995.

Leis, V., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., & Neumann, T. (2015). How good are query optimizers, really?. Proceedings of the VLDB Endowment, 9(3), 204-215. https://doi.org/10.14778/2850583.2850594.

Published

2023-01-02

How to Cite

Saiapina, I. (2023). RELATIONAL DATABASE WORK OPTIMIZATION OF THE TRANSPORT INFORMATION SYSTEM. Transport Systems and Technologies, (40), 261–266. https://doi.org/10.32703/2617-9040-2022-40-23

Issue

Section

Information, telecommunication and resource saving technologies