Storage efficiency comparison of UML models in selected database technologies
Andrii Filatov
Lublin University of Technology (Ukraine)
Paweł Flis
stercage@gmail.comLublin University of Technology (Poland)
Beata Pańczyk
Lublin University of Technology (Poland)
Abstract
.The study answers the question which database is the best choice for efficient data storage of UML models. Three products were considered: MongoDB, PostgreSQL and Neo4J. The effectiveness test consists of measurement the response time of queries that save and load data. This study also take into account the memory increase ratio during data insertion and the level of complexity of the implementation of the test data mappers used in database queries.
Keywords:
UML; MongoDB; Neo4J; PostgreSQLReferences
[1] The Coming Software Apocalypse. https://www.theatlantic.com/technology/archive/2017/09/saving -the-world-from-code/540393/ [05.05.2019].
[2] Jung M., Youn S., Bae J. Choi Y.: A Study on Data Input and Output Performance Comparison of MongoDB and PostgreSQL in the Big Data Environment. 8th International Conference on Database Theory and Application (DTA). IEEE, 2015.
[3] Plechawska-Wójcik M., Rykowski, D.: Comparison of relational, document and graph databases in the context of the web application development. 36th ISAT Conference, Part II, s. 3-13. Springer, Cham, 2015.
[4] Van der Veen J. S., Van der Waaij B., Meijer R. J.: Sensor Data Storage Performance: SQL or NoSQL, Physical or Virtual. Fifth International Conference on Cloud Computing, Honolulu, s. 431-438. IEEE, 2012.
[5] Oussous A., Benjelloun F. Z., Lahcen A. A., Belfkih S.: Comparison and classification of nosql databases for big data. International Journal of Database Theory and Application, 6, April, 2013.
[6] Shetty Deepika V., Chidimar S. J.: Comparative Study of SQL and NoSQL Databases to evaluate their suitability for Big Data Application. International Journal of Computer Science and Information Technology Research, June, s. 314-318. 2016.
[7] Kunda, D., Phiri, H.: A Comparative Study of NoSQL and Relational Database. Zambia ICT Journal. Tom 1, s. 1-4. 2017
[8] Bathla, G., Rani, R., Aggarwal, H.: Comparative study of NoSQL databases for big data storage. International Journal of Engineering & Technology, 2018.
[9] Mondal, A. S., Sanyal, M., Chattopadhyay, S., Mondal, K. C.: Comparative Analysis of Structured and Un-Structured Databases. International Conference on Computational Intelligence, Communications, and Business Analytics, s. 226-241. Springer, Singapore, March, 2017.
[10] Introduction - StarUML documentation. https://docs.staruml.io/[05.05.2019].
[2] Jung M., Youn S., Bae J. Choi Y.: A Study on Data Input and Output Performance Comparison of MongoDB and PostgreSQL in the Big Data Environment. 8th International Conference on Database Theory and Application (DTA). IEEE, 2015.
[3] Plechawska-Wójcik M., Rykowski, D.: Comparison of relational, document and graph databases in the context of the web application development. 36th ISAT Conference, Part II, s. 3-13. Springer, Cham, 2015.
[4] Van der Veen J. S., Van der Waaij B., Meijer R. J.: Sensor Data Storage Performance: SQL or NoSQL, Physical or Virtual. Fifth International Conference on Cloud Computing, Honolulu, s. 431-438. IEEE, 2012.
[5] Oussous A., Benjelloun F. Z., Lahcen A. A., Belfkih S.: Comparison and classification of nosql databases for big data. International Journal of Database Theory and Application, 6, April, 2013.
[6] Shetty Deepika V., Chidimar S. J.: Comparative Study of SQL and NoSQL Databases to evaluate their suitability for Big Data Application. International Journal of Computer Science and Information Technology Research, June, s. 314-318. 2016.
[7] Kunda, D., Phiri, H.: A Comparative Study of NoSQL and Relational Database. Zambia ICT Journal. Tom 1, s. 1-4. 2017
[8] Bathla, G., Rani, R., Aggarwal, H.: Comparative study of NoSQL databases for big data storage. International Journal of Engineering & Technology, 2018.
[9] Mondal, A. S., Sanyal, M., Chattopadhyay, S., Mondal, K. C.: Comparative Analysis of Structured and Un-Structured Databases. International Conference on Computational Intelligence, Communications, and Business Analytics, s. 226-241. Springer, Singapore, March, 2017.
[10] Introduction - StarUML documentation. https://docs.staruml.io/[05.05.2019].
Filatov, A., Flis, P., & Pańczyk, B. (2019). Storage efficiency comparison of UML models in selected database technologies. Journal of Computer Sciences Institute, 12, 193–198. https://doi.org/10.35784/jcsi.437
Authors
Andrii FilatovLublin University of Technology Ukraine
Authors
Beata PańczykLublin University of Technology Poland
Statistics
Abstract views: 282PDF downloads: 527
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.