Design and Implementation of a General-Purpose and Scalable Decision Support System Framework

İsmail Öztel; Celal Çeken

doi:10.35377/saucis...1702760

Design and Implementation of a General-Purpose and Scalable Decision Support System Framework

Abstract

Decision Support Systems facilitate timely and informed decision-making by integrating heterogeneous data sources, analytical models, and user interfaces into cohesive systems. This study introduces a general-purpose and scalable Decision Support System framework that supports just-in-time, data-driven decision-making processes across diverse application domains. The proposed system architecture incorporates essential technologies, including Apache Kafka for parallel data streaming, a Python-based distributed data analytics module, a role-based access control system for authentication and authorization, and a WebSocket interface to deliver predictions in real-time. The Iris dataset was utilized for model validation, with logistic regression employed as the predictive model. Experimental evaluations were conducted under simulated load conditions using the Artillery tool to assess system scalability and responsiveness. Results demonstrate significant performance improvements when utilizing Apache Kafka consumer group with associated worker modules for parallel processing, achieving lower mean, median, P95, and P99 latencies. These findings highlight the effectiveness of the proposed architecture in enabling highly scalable and responsive Decision Support System applications that support just-in-time decision-making.

Keywords

References

D. J. Power, Decision Support Systems: Concepts and Resources for Managers. Westport, CT: Quorum Books, 2022.
J. P. Shim, M. Warkentin, J. F. Courtney, D. J. Power, R. Sharda, and C. Carlsson, “Past, present, and future of decision support technology.” Decision Support Systems, vol. 33, no. 2, pp. 111–126, 2002.
R. H. Sprague, and E. D. Carlson, Building Effective Decision Support Systems. Englewood Cliffs, NJ: Prentice-Hall, 1982.
S. Dyapa, “Real-time fraud detection: Leveraging Apache Kafka and spark for financial transaction processing.” International Journal on Science and Technology (IJSAT), vol. 16, no. 1, pp. 1-9, 2025.
C. Martín, P. Langendoerfer, P. S. Zarrin, M. Díaz, and B. Rubio, “Kafka-ML: Connecting the data stream with ML/AI frameworks,” Future Generation Computer Systems, vol. 126, pp. 15–33, 2022. doi: 10.1016/j.future.2021.07.037
T. P. Raptis, and A. Passarella, “A Survey on networked data streaming with Apache Kafka.” IEEE Access, vol. 11, pp. 85333-85350, 2023.
C. W. Ching, et al., “Agile DART: An agile and scalable edge stream processing engine.” IEEE Transactions on Mobile Computing, vol. 24, no. 5, pp. 4510 – 4528, 2025
P. Agnihotri, B. Koldehofe, R. Heinrich, C. Binnig, and M. Luthra, “PDSP-Bench: A benchmarking system for parallel and distributed stream processing,” arXiv, 2025. doi: 10.48550/arXiv.2504.10704

M. Carella, et al., “Influence of levobupivacaine regional scalp block on hemodynamic stability, intra- and postoperative opioid consumption in supratentorial craniotomies: A randomized controlled trial,” Anesthesia & Analgesia, vol. 132, no. 2, pp. 500–511, 2021. doi: 10.1213/ANE.0000000000005230
S. Sperandei, “Understanding logistic regression analysis,” Biochem. Med. (Zagreb), vol. 24, no. 1, pp. 12–18, 2014. doi: 10.11613/BM.2014.003
E. C. Zabor, C. A. Reddy, R. D. Tendulkar, and S. Patil, “Logistic regression in clinical studies,” International Journal of Radiation Oncology, Biology, Physics, vol. 112, no. 2, pp. 271–277, 2021. doi: 10.1016/J.IJROBP.2021.08.007
Q. Wang, and G. Zhao, “ICT self-efficacy mediates most effects of university ICT support on preservice teachers' TPACK: Evidence from three normal universities in China,” British Journal of Educational Technology, vol. 52, no. 6, pp. 2319–2339, 2021. doi: 10.1111/bjet.13141
C. U. Doğruer, “Model Predictive Control Using First/Third Order Splines,” in Proc. 11th Int. Conf. Syst. Control (ICSC), 2023, pp. 541–546. doi: 10.1109/icsc58660.2023.10449727
B. Karadağ, and K. Kalkan, “IBAM: IPFS and blockchain based authentication for MQTT protocol in IoT,” in Proc. IEEE Symp. Comput. Commun. (ISCC), 2023, pp. 541–546. doi: 10.1109/ISCC58397.2023.10217960
R. A. Fisher, “Iris [Dataset],” UCI Machine Learning Repository, 1988. doi: 10.24432/C56C76arXiv+7
Apache Software Foundation: “A distributed streaming platform,” Apache Kafka Documentation, [Online]. Available: https://kafka.apache.org/documentation/ [Accessed: Mar. 15, 2026].
Artillery, “Artillery Docs,” Artillery.io. [Online]. Available: https://www.artillery.io/docs [Accessed: Mar. 15, 2026].

Details

Primary Language

English

Subjects

Empirical Software Engineering , Computer Software

Journal Section

Research Article

Authors

İsmail Öztel
0000-0001-5157-7035
Türkiye

Celal Çeken ^*
0000-0002-6912-0057
Türkiye

Early Pub Date

March 13, 2026

Publication Date

March 13, 2026

Submission Date

May 20, 2025

Acceptance Date

September 26, 2025

Published in Issue

Year 2026 Volume: 9 Number: 1

DOI

https://doi.org/10.35377/saucis...1702760

IZ

https://izlik.org/JA32LC78EN

Cite

RIS / Bibtex

APA

Öztel, İ., & Çeken, C. (2026). Design and Implementation of a General-Purpose and Scalable Decision Support System Framework. Sakarya University Journal of Computer and Information Sciences, 9(1), 1-7. https://doi.org/10.35377/saucis...1702760

AMA

1.Öztel İ, Çeken C. Design and Implementation of a General-Purpose and Scalable Decision Support System Framework. SAUCIS. 2026;9(1):1-7. doi:10.35377/saucis.1702760

Chicago

Öztel, İsmail, and Celal Çeken. 2026. “Design and Implementation of a General-Purpose and Scalable Decision Support System Framework”. Sakarya University Journal of Computer and Information Sciences 9 (1): 1-7. https://doi.org/10.35377/saucis. 1702760.

EndNote

Öztel İ, Çeken C (March 1, 2026) Design and Implementation of a General-Purpose and Scalable Decision Support System Framework. Sakarya University Journal of Computer and Information Sciences 9 1 1–7.

IEEE

[1]İ. Öztel and C. Çeken, “Design and Implementation of a General-Purpose and Scalable Decision Support System Framework”, SAUCIS, vol. 9, no. 1, pp. 1–7, Mar. 2026, doi: 10.35377/saucis...1702760.

ISNAD

Öztel, İsmail - Çeken, Celal. “Design and Implementation of a General-Purpose and Scalable Decision Support System Framework”. Sakarya University Journal of Computer and Information Sciences 9/1 (March 1, 2026): 1-7. https://doi.org/10.35377/saucis. 1702760.

JAMA

1.Öztel İ, Çeken C. Design and Implementation of a General-Purpose and Scalable Decision Support System Framework. SAUCIS. 2026;9:1–7.

MLA

Öztel, İsmail, and Celal Çeken. “Design and Implementation of a General-Purpose and Scalable Decision Support System Framework”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 1, Mar. 2026, pp. 1-7, doi:10.35377/saucis. 1702760.

Vancouver

1.İsmail Öztel, Celal Çeken. Design and Implementation of a General-Purpose and Scalable Decision Support System Framework. SAUCIS. 2026 Mar. 1;9(1):1-7. doi:10.35377/saucis. 1702760