H.3.13. Intelligent Web Services and Semantic Web
Atefeh Niroomand; Seyyed Hamid Ghafouri; Amid Khatibi Bardsiri
Abstract
This study addresses the challenges of managing dynamic and heterogeneous Internet of Things (IoT) data by proposing a time-aware recommender system that integrates a dynamic semantic ontology with clustering techniques and a hybrid collaborative filtering framework. The proposed model continuously updates ...
Read More
This study addresses the challenges of managing dynamic and heterogeneous Internet of Things (IoT) data by proposing a time-aware recommender system that integrates a dynamic semantic ontology with clustering techniques and a hybrid collaborative filtering framework. The proposed model continuously updates the ontology based on user interactions and incorporates temporal information into both knowledge representation and clustering processes, enabling adaptive and real-time modeling of evolving user behaviors.The dataset consists of approximately 500 users and 15,000 time-stamped interaction records collected over four months, including demographic attributes (age and gender), IoT device usage patterns, and temporal features such as timestamp and time of day.The recommendation framework combines ontology-enhanced user-based collaborative filtering with dynamic K-means clustering, leveraging both semantic relationships and behavioral similarities to improve recommendation quality. Experimental evaluation is conducted using Precision, Recall, F1-score, Accuracy, MAE, and RMSE metrics. The model achieves improvements ranging from approximately 2% to 52%, with respect to state-of-the-art non-temporal methods and traditional collaborative filtering techniques, respectively.Furthermore, computational complexity analysis indicates that the additional processing cost introduced by dynamic ontology updates and temporal modeling remains manageable, preserving the practical applicability of the proposed framework in resource-constrained IoT environments.
H.3.13. Intelligent Web Services and Semantic Web
E. Shahsavari; S. Emadi
Abstract
Service-oriented architecture facilitates the running time of interactions by using business integration on the networks. Currently, web services are considered as the best option to provide Internet services. Due to an increasing number of Web users and the complexity of users’ queries, simple ...
Read More
Service-oriented architecture facilitates the running time of interactions by using business integration on the networks. Currently, web services are considered as the best option to provide Internet services. Due to an increasing number of Web users and the complexity of users’ queries, simple and atomic services are not able to meet the needs of users; and to provide complex services, it requires service composition. Web service composition as an effective approach to the integration of business institutions’ plans has taken significant acceleration. Nowadays, web services are created and updated in a moment. Therefore, in the real world, there are many services which may not have composability according to the conditions and constraints of the user's preferred choice. In the proposed method for automatic service composition, the main requirements of users including available inputs, expected outputs, quality of service, and the priority are initially and explicitly specified by the user and service composition is done with this information. In the proposed approach, due to a large number of services with the same functionality, at first, the candidate services are reduced by the quality of service-based Skyline method, and moreover, by using an algorithm based on graph search, all possible solutions will be produced. Finally, the user’s semantic constraints are applied on service composition, and the best composition is offered according to user’s requests. The result of this study shows that the proposed method is more scalable and efficient, and it offers a better solution by considering the user’s semantic constraints.
