Volume 12 • Issue 2 • PP: 01–09 • 2025
Optimized Composition of Business Process Web Services via QoS-Based Categorization Using Decision Tree Classifier and Knowledge-Based Decision Support
Open Access & Copyright
© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
Determining web services according to Quality of Service (QoS) restrictions is the topic of discussion in this section. Decision tree classifiers are used to accomplish this classification. Because of the ever-changing and expanding nature of online services, it is necessary to accurately categorize them in order to make choosing them more efficient for consumers. It makes use of decision tree techniques, more especially the C5.0 classifier, this is an advancement over older approaches such as the C4.5 classifiers. It incorporates characteristics like as noisy handling, incomplete information administration, and improved decision-making correctness. Web services are classified into four distinct groups: Outstanding, Good, Average, and Poor. These classifications are determined by QoS metrics that include time to response, accessibility, performance, dependability, and success rate. The choice of features is accomplished utilizing an evolutionary algorithm with a wrapper technique with the goal to maximize the effectiveness of this category. This method minimizes the number of repetitive features and improves the method of classification for the purpose of optimization. The resilience and predicted reliability of the algorithm are ensured by additional approaches like as cross-validation and error reduction. These approaches also address difficulties such as overfitting and redundant characteristics. The construction of integrated web services for complicated corporate operations is a particularly valuable use of this technology, which also considerably improves the procedure for making choices for identifying services and consumption. Service 7 stands out with an impressive 98% performance, while Service 6 and Service 3 are also among the top-performing services. Compared to the others, Service 1, Service 2, Service 5, and Service 4 all exhibit comparatively poor results.
Keywords
References
[1] J. Tanha, M. van Someren, and H. Afsarmanesh, “Semisupervised self-training for decision tree classifiers,” International Journal of Machine Learning and Cybernetics, vol. 8, no. 1, pp. 355–370, Feb. 2017.
[2] E. Mohamed, “The relationship between artificial intelligence and internet of things: A quick review,” Journal of Cybersecurity and Information Management, vol. 1, no. 1, pp. 30–34, 2020.
[3] A. B. Gadicha and V. B. Gadicha, “Implicit authentication approach by generating strong password through visual key cryptography,” Journal of Cybersecurity and Information Management, vol. 1, no. 1, pp. 5–16, 2020.
[4] B. S. Balaji, S. Balakrishnan, K. Venkatachalam, and V. Jeyakrishnan, “Retracted article: Automated query classification based web service similarity technique using machine learning,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 6, pp. 6169–6180, Jun. 2021.
[5] M. S. Das, A. Govardhan, and D. V. Lakshmi, “Classification of web services using data mining algorithms and improved learning model,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 17, no. 6, pp. 3191–3198, Dec. 2019.
[6] M.-T. Wu, “Confusion matrix and minimum crossentropy metrics based motion recognition system in the classroom,” Scientific Reports, vol. 12, no. 1, pp. 1–10, Feb. 2022.
[7] C. Vivek, M. Indu, and N. Nandhini, “Speech recognition using artificial neural network,” Journal of Cognitive Human-Computer Interaction, vol. 5, no. 2, pp. 8–14, 2023.
[8] V. Narayanan, P. Nithya, and M. Sathya, “Effective lung cancer detection using deep learning network,” Journalof Cognitive Human-Computer Interaction, vol. 5, no. 2, pp. 15–23, 2023.
[9] S. Ruuska, W. Hämäläinen, S. Kajava, M. Mughal, P. Matilainen, and J. Mononen, “Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle,” Behavioural Processes, vol. 148, pp. 56–62, Mar. 2018.
[10] N. Agarwal, G. Sikka, and L. K. Awasthi, “Enhancing web service clustering using length feature weight method for service description document vector space representation,” Expert Systems with Applications, vol. 161, p. 113682, Dec. 2020.
[11] M. B. Blake, W. Cheung, M. C. Jaeger, and A. Wombacher, “WSC-06: The web service challenge,” in Proceedings of the 8th IEEE International Conference on ECommerce Technology and the 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services, Jun. 2006, p. 62.
[12] D. A. Pustokhin and I. V. Pustokhina, “FLC-NET: Federated lightweight network for early discovery of malware in resource-constrained IoT,” International Journal of Wireless and Ad Hoc Communication, vol. 6, no. 2, pp. 43–55, 2023.
[13] M. Saber, E.-S. M. El-Kenawy, A. Ibrahim, M. M. Eid, and A. A. Abdelhamid, “Metaheuristic optimized ensemble model for classification of SMS spam in computer networks,” International Journal of Wireless and Ad Hoc Communication, vol. 6, no. 2, pp. 56–64, 2023.
[14] A. V. Dastjerdi and R. Buyya, “Compatibility-aware cloud service composition under fuzzy preferences of users,” IEEE Transactions on Cloud Computing, vol. 2, no. 1, pp. 1–13, Jan. 2014.
[15] M. Razian, M. Fathian, H.Wu, A. Akbari, and R. Buyya, “SAIoT: Scalable anomaly-aware services composition in CloudIoT environments,” IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3665–3677, Mar. 2020.
[16] M. Gao, M. Chen, A. Liu, W. H. Ip, and K. L. Yung, “Optimization of microservice composition based on artificial immune algorithm considering fuzziness and user preference,” IEEE Access, vol. 8, pp. 26 385– 26 404, 2020.
[17] R. Buyya, S. N. Srirama, G. Casale, R. Calheiros, Y. Simmhan, B. Varghese, E. Gelenbe, B. Javadi, L. M. Vaquero, and M. A. Netto, “A manifesto for future generation cloud computing: Research directions for the next decade,” ACM Computing Surveys, vol. 51, no. 5, pp. 1–38, 2018.
[18] H. S. Salih and F. A. Mohamed, “Fusion-based diversified model for internet of vehicles: Leveraging artificial intelligence in cloud computing,” Fusion: Practice and Applications, vol. 12, no. 2, pp. 54–69, 2023.
[19] Z. N. Al-kateeb and D. B. Abdullah, “A smart architecture leveraging fog computing fusion and ensemble learning for prediction of gestational diabetes,” Fusion: Practice and Applications, vol. 12, no. 2, pp. 70–87, 2023.
[20] G. Chen, T. Jiang, M.Wang, X. Tang, andW. Ji, “Modeling and reasoning of IoT architecture in semantic ontology dimension,” Computer Communications, vol. 153, pp. 580–594, Mar. 2020.
[21] W. Chen, B. Liu, H. Huang, S. Guo, and Z. Zheng, “When UAV swarm meets edge-cloud computing: The QoS perspective,” IEEE Network, vol. 33, no. 2, pp. 36–43, mar/apr 2019.
[22] S. K. Gavvala, C. Jatoth, G. R. Gangadharan, and R. Buyya, “QoS-aware cloud service composition usingeagle strategy,” Future Generation Computer Systems, vol. 90, pp. 273–290, Jan. 2019.
[23] M. Vucetic, M. Hudec, and B. Bozilovic, “Fuzzy functional
dependencies and linguistic interpretations employed in knowledge discovery tasks from relational databases,” Engineering Applications of Artificial Intelligence, vol. 88, p. 103395, Feb. 2020.
[24] M. Sozat and A. Yazici, “A complete axiomatization for fuzzy functional and multivalued dependencies in fuzzy database relations,” Fuzzy Sets and Systems, vol. 117, no. 2, pp. 161–181, Jan. 2001.
[25] B. Sheng, O. M. Moosman, J. Del Pozo-Cruz, B. Del Pozo-Cruz, R. M. Alfonso-Rosa, and Y. Zhang, “A comparison of different machine learning algorithms, types and placements of activity monitors for physical activity classification,” Measurement, vol. 154, p. 107480, Mar. 2020.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.