Recover Data about Detected Defects of Underground Metal Elements of Constructions in Amazon Elasticsearch Service

Roman Mysiuk, Volodymyr Yuzevych


This paper examines data manipulation in terms of data recovery using cloud computing and search engine. Accidental deletion or problems with the remote service cause information loss. This case has unpredictable consequences, as data must be re-collected. In some cases, this is not possible due to system features. The primary purpose of this work is to offer solutions for received data on detected defects of underground metal structural elements using modern information technologies.

The main factors that affect underground metal structural elements' durability are the soil environment's external action and constant maintenance-free use. Defects can usually occur in several places, so control must be carried out along the entire length of the underground network. To avoid the loss of essential data, approaches for recovery using Amazon Web Service and a developed web service based on the REST architecture are considered. The general algorithm of the system is proposed in work to collect and monitor data on defects of underground metal structural elements. The result of the study for the possibility of data recovery using automatic snapshots or backup data duplication for the developed system.



recovering data; search; cloud computing; snapshots; corrosion; defects; underground metal elements of construction

Full Text:



1. Yuzevych, L., Skrynkovskyy, R., Yuzevych, V., Lozovan, V., Pawlowski, G., Yasinskyi, M., & Ogirko, I. (2019). Improving the diagnostics of underground pipelines at oil and gas enterprises based on determining hydrogen exponent (PH) of the soil media applying neural networks. Eastern-European Journal of Enterprise Technologies, 4(5 ), 56–64. doi: 10.15587/1729-4061.2019.174488

2. Yuzevych, L., Yankovska, L., Sopilnyk, L., Yuzevych, V., Skrynkovskyy, R., Koman, B., Yasinska-Damri, L., Heorhiadi, N., Dzhala, R., & Yasinskyi, M. (2019). Improvement of the toolset for diagnosing underground pipelines of oil and gas enterprises considering changes in internal working pressure. Eastern-European Journal of Enterprise Technologies, 6(5), 23–29. doi: 10.15587/1729-4061.2019.184247

3. Yuzevych, L., Skrynkovskyy, R., & Koman, B. (2017). Development of information support of quality management of underground pipelines. EUREKA: Physics and Engineering, 4, 49–60. doi: 10.21303/2461-4262.2017.00392

4. Gormley, C., & Tong, Z. (2015). Elasticsearch The Definitive Guide: A Distributed Real-time Search and Analytics Engine. Retrieved from

5. Kononenko, O., Baysal, O., Holmes, R., & Godfrey, M. W. (2014). Mining modern repositories with elasticsearch. Proceedings of the 11th Working Conference on Mining Software Repositories, 328–331. doi: 10.1145/2597073.2597091

6. Taylor, R., Ali, M. H., & Varley, I. (2018). Automating the processing of data in research. A proof of concept using elasticsearch. International Journal of Surgery, 55, S41. doi: 10.1016/j.ijsu.2018.05.179

7. Elastic. (n. d.). Bulk API. Retrieved from

8. Amazon Web Services. (n. d.). Analysing Text with Amazon Elasticsearch Service and Amazon Comprehend. Retrieved from

9. AKCA, M. A., Aydoğan, T., & İlkuçar, M. (2016). An Analysis on the Comparison of the Performance and Configuration Features of Big Data Tools Solr and Elasticsearch. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 8–12. doi: 10.18201/ijisae.271328

10. Bahls, D., Zapilko, B., & Tochtermann, K. (2013). A Data Restore Model for Reproducibility in Computational Statistics. Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies, Article No. 13, 1–18. doi: 10.1145/2494188.2494205

11. Amazon Web Services. (2023). Amazon OpenSearch Service: Developer Guide. Retrieved from

12. Mane, D., Chitnis, K., & Ojha, N. (2013). The spring framework: an open source java platform for developing robust Java applications. Journal of Innovative Technology and Exploring Engineering, 3, 137–143.

13. Thacker, U., Pandey, M., & Rautaray, S. S. (2017). Review of Elasticsearch Performance Variating the Indexing Methods. Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, 3–8. doi: 10.1007/978-981-10-3376-6_1

14. Thomas, M. A., & Redmond, R. T. (2009). From the Client-Server Architecture to the Information Service Architecture. AMCIS 2009 Proceedings. 115.

15. Wu, W., Kang, R., & Li, Z. (2015). Risk assessment method for cyber security of cyber physical systems. 2015 First International Conference on Reliability Systems Engineering. doi: 10.1109/icrse.2015.7366430

16. Elastic. (n. d.). Quantitative Cluster Sizing. Retrieved from

17. Tencent Cloud. (2022). Elasticsearch Service Best Practices Product Documentation. Retrieved from

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM


  • There are currently no refbacks.

Copyright (c) 2023 Roman Mysiuk, Volodymyr Yuzevych

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.