1. CSN
  2. All Volumes and Issues
  3. Volume 6, Number 1, 2024
  4. Development of a Hybrid Method for Data Warehouse Construction

Development of a Hybrid Method for Data Warehouse Construction

CSN.
2024;
Volume 6, Number 1
: pp. 67 - 80
https://doi.org/10.23939/csn2024.01.067
Authors:
  1. Оleh M. Koval
  2. Oleh Harasymchuk
1
Lviv Polytechnic National University, Information Security Department
2
Lviv Polytechnic National University, Information Security Department

The examined approach to building an adaptive and convenient data warehouse goes beyond simple data storage, focusing on processing data for various types of reports. A hybrid concept for constructing the data warehouse is proposed, combining the advantages of existing methodologies and providing optimal interaction with the data warehouse for all users. The proposed method allows for the quick deployment, adaptive maintenance, and easy scalability of the data warehouse in the future.

This proprietary hybrid concept includes centralized data storage and the distribution of detailed data into small lookup tables for improved and faster warehouse functionality. The method is also characterized by a faster system deployment due to a limited number of connections between tables.

Using an online store as an example, all the benefits of the proposed method were demonstrated, both in data recording and processing, as well as in utilizing the data for future reports. The approach proves to be a promising and effective solution for companies seeking an optimal compromise between traditional methodologies and modern business requirements.

data warehouse
concept Inmon
concept Kimbal
hybrid concept
  1. Minukhin S., Fedko V., & Gnusov Y. Enhancing the performance of distributed big data processing systems using Hadoop   and   PolyBase.   Eastern-European   Journal   of   Enterprise   Technologies,   4(2–94),   (2018),   pp.   16–28.DOI:10.15587/1729-4061.2018.139630.
  2. Praveen Kumar, Dr. Kavita The Study On Data Warehousing Different Concepts, Vol. 21, No. 16, (2019), pp. 3103–3109. Available at: http://gujaratresearchsociety.in/index.php/JGRS/article/view/3497 (Accessed: 10 March 2024).
  3. Inmon W. H. Building the Data Warehouse,3rd Edition (3rd. ed.). John Wiley & Sons, Inc., USA. 2002. Avaible at:      https://fit.hcmute.edu.vn/Resources/Docs/SubDomain/fit/ThayTuan/DataWH/B... (Accessed: 10 March 2024)
  4. Bhatia P. (2019). Data Mining and Data Warehousing: Principles and Practical Techniques. Cambridge University Press, Cambridge. DOI:10.1017/9781108635592
  5. Padmaja Potinen/ Oracle Database Data Warehousing Guide, 21c. Copyright © 2001, 2022, Oracle and/or its affiliates. Available at https://docs.oracle.com/en/database/oracle/oracle-database/21/dwhsg/pref... 9CDC42C7-5BB2-4433-9F3E-ADE92929A0EA (Accessed: 10 March 2024).
  6. Simitsis A., Skiadopoulos S., & Vassiliadis P. The History, Present, and Future of ETL Technology. Proceedings of the 25th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP) co-located with the 26th International Conference on Extending Database Technology and the 26th International Conference on Database Theory (EDBT/ICDT 2023), Ioannina, Greece, March 28, 2023. Available at: https://ceur- ws.org/Vol-3369/invited1.pdf (Accessed: 10 March 2024).
  7. Jaganathan Manonmani, Arun. Research Paper: The role of Software architecture for the design of scalable and secure Bigdata in Banking Sectors The role of Software architecture for the design of scalable and secure Bigdata in Banking Sectors. (2023). Available at: https://www.researchgate.net/publication/371491773_Survey_Paper_The_ role_of_Software_architecture_for_the_design_of_scalable_and_secure_Bigdata_in_Banking_Sectors_The_role_of_Softw are_architecture_for_the_design_of_scalable_and_secure_Bigdata_in_Banki (Accessed: 10 March 2024).
  8. Building a data warehouse: A step-by-step guide. Available at: https://www.n-ix.com/building-a-data- warehouse/ . (Accessed: 25 February 2024).
  9. Aberer K., Hemm K. A Methodology for Building a Data Warehouse in a Scientific Environment, Cooperative Information Systems, 1996. Proceedings., First IFCIS International Conference, DOI: 10.1109/COOPIS.1996.555001
  10. Manole V., Matei G. Building a Data Warehouse step by step DOAJ, 2007. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1028461 (Accessed: 10 March 2024).
  11. Gardner D. R. Building the Data Warehouse Communications Of The Acm September 1998/Vol. 41, No. 9. Available at: https://web.archive.org/web/20060519201128id_/http://www.csun.edu:80/~ch... Appalachian/Database/ Data%20Warehouse/Building%20a%20Data%20Warehouse.pdf (Accessed: 10 March 2024).
  12. Bal B. Building Machine Learning Warehouse -A Myth or Reality 2023. Available at: https://www.researchgate.net/publication/370902095_Machine_Learning_Warehouse_-A_Myth_or_Reality  (Accessed  at 10 March 2024).
  13. Vaisman Alejandro Ariel and Esteban Zimányi. “ata Warehouse Systems: Design and Implementation. Data Warehouse Systems (2022): n. pag. DOI:10.1007/978-3-662-65167-4
  14. Томашевський В. М. Особливості проектування гібридних сховищ даних з врахуванням джерел даних / В. М. Томашевський, А. Ю. Яцишин // Вісник Національного університету “Львівська політехніка”. 2011. № 715 : Інформаційні системи та мережі. С. 246–254. Available at: https://science.lpnu.ua/uk/sisn/vsi-vypusky/vypusk-715- 2011/osoblyvosti-proektuvannya-gibrydnyh-shovyshch-danyh-z-vrahuvannyam (Accessed: 10 March 2024).
  15. El Moukhi N., El Azami I., Hajbi S. Towards a new hybrid approach for building document-oriented data warehouses. International Journal of Electrical and Computer Engineering (IJECE) 12(6), 2022. DOI: 10.11591/ijece.v12i6.pp6423-6431