A model of the decision support system for managing the process of growing vegetables in a greenhouse

Authors

  • M. Lendiel National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • T. Lendiel National University of Life and Environmental Sciences of Ukraine image/svg+xml

DOI:

https://doi.org/10.31548/energiya3(73).2024.051

Abstract

The developed of automatic control systems provide constant monitoring of technological indicators in greenhouses, as well as reporting on the current state in real time and conducting analysis based on available data.

Having this data in the system, the manufacturer can analyze all key indicators, their changes and impact over time and make appropriate decisions for their enterprise. However, the created systems expand over time, and accordingly the information in them also expands, so it is necessary to effectively analyze previously entered data. In this case, there is a need to create a system that will analyze indicators based on accumulated data. It is proposed to carry out analysis using OLAP and Data Mining technologies.

The purpose of the research is to implement a data warehouse of a decision support system using Data Mining technology to increase the efficiency of growing vegetables in closed soil structures.

In the process of developing an automated control system, a storage model of these decision support systems was developed. In the work, the structure of the dynamic database was developed using the time series algorithm. At the same time, data input, storage and analysis modules were created. The use of Data Mining technology for the analysis of large volumes of information was proposed. The obtained results of the system can be used in the process of forming management decisions for managing technological processes in the greenhouse economy. This will allow you to direct the management strategy of individual business processes in such a way as to increase the yield in greenhouses and, accordingly, the profitability of the farm as a whole.

Key words: database, monitoring, Data Mining, data storage, greenhouse

References

Lysenko, V., Bolbot, I., Lendiel, T., Koval V., Nakonechnyy I. (2022). Genetic Algorithm in Optimization Problems for Greenhouse Facilities, IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine, 185-188, doi: 10.1109/CSIT56902.2022.10000750.

Silberschatz, Abraham; Korth, Henry F., Sudarshan, S. (2011). Database system concepts (V. 6). New York: McGraw-Hill. ISBN 978-0-07-352332-3. OCLC 436031093.

Lendiel, T., Lysenko, V., Nakonechna, K. (2020). Computer-integrated technologies for fitomonitoring in the greenhouse. In Data-Centric Business and Applications: ICT Systems-Theory, Radio-Electronics, Information Technologies and Cybersecurity, 5, 711-729. Cham: Springer International Publishing.

Tsiutsiura, S. V., Kryvoruchko, V. V., Tsiutsiura, M. I. (2012). Kliuchovi pokaznyky efektyvnosti. [Key performance indicators]. Pryntsypy rozrobky kliuchovykh pokaznykiv dlia biudzhetnoi sfery Upravlinnia rozvytkom skladnykh system, 10, 87-91.

Key Performance Indicators (KPIs) in Multidimensional Models. Available at: https://docs.microsoft.com/en-us/analysis-services/multidimensional-models/key-performance-indicators-kpis-in-multidimensional-models?view=asallproducts-allversions

Kroenke, D. M., Auer, D. J., Vandenberg, S. L., & Yoder, R. C. (2010). Database concepts, 1480-1486. Upper Saddle River, NJ: Prentice Hall.

Lysenko, V., Lendiel, T., Bolbot, I., Nakonechnyy, I. (2022). Neural Network Structures for Energy-efficient Control of Energy Flows in Greenhouse Facilities," 2022 IEEE 9th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T), Kharkiv, Ukraine, 21-26, doi: 10.1109/PICST57299.2022.10238512.

W. Chen, X. Bo (2011). Dynamic modulating strategy of materialized views in data warehouse, The 3rd International Conference on Data Mining and Intelligent Information Technology Applications, Macao, China, 102-104.

Naeem, M. A. , Mirza, F., Khan, H. U., Sundaram, D., Jamil,N.,Weber, G. Big (2020). Data Velocity Management–From Stream to Warehouse via High Performance Memory Optimized Index Join, in IEEE Access, 8, 195370-195384, doi: 10.1109/ACCESS.2020.3033464.

X. Li, M. Yang, X. Xia, K. Zhang, K. Liu, (2022). A Distributed Data Fabric Architecture based on Metadate Knowledge Graph," 2022 5th International Conference on Data Science and Information Technology (DSIT), Shanghai, China, 1-7, doi: 10.1109/DSIT55514.2022.9943831.

Downloads

Published

2024-10-10

Issue

No. 3 (2024)

Section

Статті

License

Relationship between right holders and users shall be governed by the terms of the license Creative Commons  Attribution – non-commercial – Distribution On Same Conditions 4.0 international (CC BY-NC-SA 4.0):https://creativecommons.org/licenses/by-nc-sa/4.0/deed.uk

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).