مروری بر روش های کشف و انتخاب خودکار سرویس وب در معماری سرویسگرا
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسی
هانیه حسینی
1
,
اسماعیل خیرخواه
2
1 - گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
2 - گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
کلید واژه: معماری سروریس گرا, کشف سرویس, انتخاب سرویس, سرویس وب,
چکیده مقاله :
این مقاله به بررسی رویکردهای مختلف برای کشف خودکار سرویس های وب می پردازد. کشف سرویس به عنوان فرایند شناسایی سرویس های جدید در اینترنت، نقش حیاتی در بهبود عملکرد و کیفیت سیستم ها ایفا می کند. این فرایند همچنان با چالش هایی مانند سازگاری با محیط های پویا، افزایش دقت و کاهش هزینه ها روبرو است. چهار رویکرد برای کشف خودکار سرویس معرفی نمودیم. رویکرد مبتنی بر معنا بر تحلیل معنایی داده ها و ارتباطات بین سرویس ها تمرکز دارد و می تواند منجر به درک عمیق تر و دقت بیشتر شود. رویکرد مبتنی بر روش های بهینه سازی از الگوریتم های بهینه سازی برای یافتن راه حل بهینه استفاده می کند و می تواند کارایی و هزینه را بهبود دهد. رویکرد مبتنی بر اینترنت اشیا اجتماعی با استفاده از تعامل بین دستگاه ها به کشف سرویس ها می پردازد. رویکرد مبتنی بر یادگیری ماشین نیز از تکنیکهای یادگیری ماشین برای کشف سرویس بهره می برد.درنهایت، مقاله بیان می کند که رویکرد مبتنی بر یادگیری ماشین به عنوان مهمترین رویکرد کنونی برای کشف خودکار سرویس شناسایی شده و مسیر آینده پژوهش می تواند شامل توسعه الگوریتم های مبتنی بر یادگیری عمیق و ترکیب با رویکردهای معنایی باشد.
This article examines various approaches to automatic discovery of web services. The article first discusses the concept of service discovery and its importance in the development and deployment of software systems. Service discovery, as the process of identifying and analyzing new services in the internet, plays a vital role in improving system performance and quality. Despite existing advancements, this process still faces challenges such as compatibility with dynamic environments, increasing accuracy, and reducing costs. The article then introduces four main approaches to automatic service discovery. The semantics-based approach focuses on analyzing semantic data and connections between services, leading to a deeper understanding and increased precision. The optimization-based approach uses optimization algorithms to find optimal solutions and improve efficiency and costs. The social Internet of Things approach focuses on using interaction and collaboration between devices to discover services. The machine learning approach utilizes advanced machine learning techniques for analyzing data and discovering services. Ultimately, the article states that the machine learning approach is identified as the most important current approach for automatic service discovery, and the future research direction in this area may include developing algorithms based on deep learning and combining them with semantic approaches.
[1] H. Moeini, I. Yen, and F. Bastani, "Summarization in semantic based service discovery in dynamic iot-edge networks," arXiv preprint arXiv:2009.02858, 2020.
[2] S. Garba, R. Mohamad, and N. A. Saadon, "Meta-Context Ontology for Self-Adaptive Mobile Web Service Discovery in Smart Systems," International Journal of Information Technologies and Systems Approach (IJITSA), vol. 15, no. 2, pp. 1-26, 2022.
[3] Y. Li and B. Starly, "Building a Knowledge Graph to Enrich Chatgpt Responses in Manufacturing Service Discovery," Available at SSRN 4517533, 2023.
[4] S. Berrani, A. Yachir, B. Djamaa, S. Mahmoudi, and M. Aissani, "Towards a new semantic middleware for service description, discovery, selection, and composition in the Internet of Things," Transactions on Emerging Telecommunications Technologies, vol. 33, no. 9, p. e4544, 2022.
[5] Z. Huang and W. Zhao, "A semantic matching approach addressing multidimensional representations for web service discovery," Expert Systems with Applications, vol. 210, p. 118468, 2022.
[6] M. Kaouan, D. Bouchiha, S. M. Benslimane, and S. Boukli-Hacene, "Towards Service Ontology for Web Services Storage and Discovery," in 2020 4th International Symposium on Informatics and its Applications (ISIA), 2020: IEEE, pp. 1-6.
[7] N. Swetha and G. Karpagam, "Lexicon ontology driven concept lattice framework for semantic web service discovery," in 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022: IEEE, pp. 1428-1435.
[8] C. Pushpa, G. Deepak, A. Kumar, J. Thriveni, and K. Venugopal, "OntoDisco: improving web service discovery by hybridization of ontology focused concept clustering and interface semantics," in 2020 IEEE international conference on electronics, computing and communication technologies (CONECCT), 2020: IEEE, pp. 1-5.
[9] J. B. Merin, W. A. Banu, and K. F. S. Shalin, "Semantic Annotation Based Effective and Quality Oriented Web Service Discovery."
[10] H. Baumgärtel, P. Moder, N. Ramzy, and H. Ehm, "Service-based Semiconductor Manufacturing using the Digital Reference Ontology for Global Service Discovery," in IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, 2020: IEEE, pp. 4533-4540.
[11] X. Fang, "Semantic clustering analysis for web service discovery and recognition in Internet of Things," Soft Computing, vol. 27, no. 3, pp. 1751-1761, 2023.
[12] Z. B. AZIZOU, A. BOUDRIES, and A. Mourad, "Grey Wolf Optimizer-based decentralized service discovery in Internet of Things applications," 2022.
[13] X. Liu and Y. Deng, "A new QoS-aware service discovery technique in the Internet of Things using whale optimization and genetic algorithms," Journal of Engineering and Applied Science, vol. 71, no. 1, p. 4, 2024.
[14] H. Liang, B. Ding, Y. Du, and F. Li, "Parallel optimization of QoS-aware big service processes with discovery of skyline services," Future Generation Computer Systems, vol. 125, pp. 496-514, 2021.
[15] M. Malekshahi Rad, A. M. Rahmani, A. Sahafi, and N. N. Qader, "Community detection and service discovery on Social Internet of Things," International Journal of Communication Systems, vol. 36, no. 11, p. e5501, 2023.
[16] Y. Guo, L. Liu, J. Panneerselvam, and R. Zhu, "Efficient service discovery in mobile social networks for smart cities," Computing, vol. 103, pp. 183-209, 2021.
[17] A. Pliatsios, D. Lymperis, and C. Goumopoulos, "S2NetM: A Semantic Social Network of Things Middleware for Developing Smart and Collaborative IoT-Based Solutions," Future Internet, vol. 15, no. 6, p. 207, 2023.
[18] A. M. Esfahani, A. M. Rahmani, and A. Khademzadeh, "Msiot: Mobile social internet of things, a new paradigm," in 2020 10th International Symposium onTelecommunications (IST), 2020: IEEE, pp. 187-193.
[19] M. J. Aslam, S. Din, J. J. Rodrigues, A. Ahmad, and G. S. Choi, "Defining service-oriented trust assessment for social internet of things," IEEE Access, vol. 8, pp. 206459-206473, 2020.
[20] A. Hamrouni, H. Ghazzai, and Y. Massoud, "Service discovery in social internet of things using graph neural networks," in 2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS), 2022: IEEE, pp. 1-4.
[21] A. Hamrouni, A. Khanfor, H. Ghazzai, and Y. Massoud, "Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects," IEEE Access, vol. 10, pp. 107330-107345, 2022.
[22] S. S. Sefati, B. Arasteh, S. Halunga, O. Fratu, and A. Bouyer, "Meet User’s Service Requirements in Smart Cities Using Recurrent Neural Networks and Optimization Algorithm," IEEE Internet of Things Journal, 2023.
[23] S. Jalal, D. K. Yadav, and C. S. Negi, "Web service discovery with incorporation of web services clustering," International Journal of Computers and Applications, vol. 45, no. 1, pp. 51-62, 2023.
[24] S. Rangarajan, "Qos-based Web service discovery and selection using machine learning," arXiv preprint arXiv:1807.01439, 2018.
[25] M. Merzoug, A. Etchiali, F. Hadjila, and A. Bekkouche, "Effective Service Discovery based on Pertinence Probabilities Learning," International Journal of Advanced Computer Science and Applications, vol. 12, no. 9, 2021.
[26] S. G. Tabrizi, N. J. Navimipour, A. S. Danesh, and S. Yalcın, "A New Decision-Making Method for Service Discovery and Selection in the Internet of Things Using Flower Pollination Algorithm," Wireless Personal Communications, vol. 126, no. 3, pp. 2447-2468, 2022.
[27] K. Zeng and I. Paik, "Semantic service clustering with lightweight bert-based service embedding using invocation sequences," IEEE Access, vol. 9, pp. 54298-54309, 2021.
[28] X. Zhang, J. Liu, M. Shi, and B. Cao, "Word embedding-based web service representations for classification and clustering," in 2021 IEEE International Conference on Services Computing (SCC), 2021: IEEE, pp. 34-43.
