DATA STORAGE OPTIMIZATION IN WEB APPLICATIONS USING DWH MODELS

M. Fostyak, L. Demkiv

Abstract


Large volumes of data that are accumulated in companies can be used to identify trends in the development of companies, understand the behavior of companies' customers, and determine business strengths and weaknesses. With the help of data warehouses, perform complex analytical queries and create reports based on this data. Large companies have data analysis and BI analytics departments to create reports, dashboards and dashboards and help management and other stakeholders gain access to critical information.

In the work, it is proposed to use a data storage model for data storage in a web application that implements the interaction between an athlete and a coach. Analysis and statistics of training results are important both for athletes who seek to improve their achievements and achieve high results in their discipline, and for coaches. Progressive web technologies and the React library were used to develop the web application. The data storage strategy takes into account the specifics of pwa application development and data engineering models for building data warehouses.

The application uses methods of data transformation using the ETL process for their meaningful storage on the server and client. At the stage of transformation, data on unimportant types of tasks are removed: warm-up, hitch, stretching, massage. Aggregation of data by types of exercises takes place and percentages of performed exercises are calculated. Comments and parameters about the achieved results are read and saved in a structured format: distance, pace, heart rate. After the data is transformed, it is loaded into the data warehouse model. A storage for the coach is built on the server based on the DWH star model. A data storage model for an athlete was created on the client using IndexedDB. Two data warehouses were built. These stores optimally store a larger amount of data and provide an opportunity to write simpler queries to obtain analytical reports than in the case of writing queries to the database. been

Keywords: pwa, React, DWH data storage models, IndexedDB, data analysis, dashboard.


References


  1. Ratten V. Sport Data Analytics and Social Media: A Process of Digital Transformation, Sport Entrepreneurship, Emerald Publishing Limited, Bingley, 2020, p. 107-119. DOI: https://doi.org/10.1108/978-1-83982-836-220201016
  2. Novak M., Rabuzin K. Prototype of web ETL tool International // Journal of Advanced Computer Science and Applications 5(6), 2014. DOI: 10.14569/IJACSA.2014.050614
  3. EI_Sappagh S., Hendawi A., EI_Bastawissy A proposed model for data warehouse ETL processes // Journal of King Saud University - Computer and Information Sciences 23(2):91–104, 2011. DOI: 10.1016/j.jksuci.2011.05.005
  4. Hanine M., Lachgar M., Elmahfoudi S., Boutkhoum O. MDA Approach for Designing and Developing Data Warehouses: A Systematic Review & Proposal // International journal of online and biomedical engineering Vol.17, N 10, 2023. DOI: https://doi.org/10.3991/ijoe.v17i10.24667
  5. Dhaouadi A., Bousselmi K., Gammoudi M., Monnet S., Hammoudi S. Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons// Data 2022, 7(8), 113. DOI: https://doi.org/10.3390/data7080113
  6. Souibgui M., Atigui F., Zammali S., Cherfi S., Yahia S. Data quality in ETL process: A preliminary study // Procedia Computer Science Volume 159, 2019, P.676-687. https://doi.org/10.1016/j.procs.2019.09.223




DOI: http://dx.doi.org/10.30970/eli.23.4

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