Sharing and reusing the big data in relational databases in a semantic way have become a big challenge. In this paper, we propose a new approach to enable semantic Web applications to access relational databases (RDBs) and their contents by semantic methods. Domain ontologies can be used to formulate RDB schema and data in order to simplify the mapping of the underlying data sources. Our method consists of two main phases: building ontology from an RDB schema and the generation of ontology instances from an RDB data automatically. In the first phase, we studied different cases of RDB schema to be mapped into ontology represented in RDF(S)-OWL, while in the second phase, the mapping rules are used to transform RDB data to ontological instances represented in RDF triples. Our approach is demonstrated with examples and validated by ontology validator.

This work proposes a robust convergence-based approach that integrates blockchain and edge computing, supporting a proactive and adaptive secure-optimized framework to fortify security measures and mitigate unauthorized access in IoT systems. In the developed framework the blockchain technology enforces proactive security by ensuring data integrity, decentralized authentication, and resilient threat prevention. While the fusion of an Artificial Neural Network (ANN) and Aquila Optimizer (AO), enables the framework to enhance the accuracy of threat detection by fine-tuning parameters and optimizing the network’s architecture. The ANN adeptly captures intricate patterns and features from input data, while the AO intelligently adjusts critical hyperparameters to foster swift convergence and achieve peak network performance. This integration empowers a robust and accurate security detection system, enabling effective identification of complex and multifaceted security threats. Through extensive experimental analyses, we validate the effectiveness of the proposed ANN-AO framework, demonstrating a reduction in latency by 587 ms, 1187 ms, 1079 ms, and 460 ms, while improving security detection by 17.00%, 41.00%, 11.00%, and 8.00%, when compared to cutting-edge security methods. Moreover, the results are further verified and validated through statistical and computational analyses.

Task execution in cloud computing requires obtaining stored data from remote data centers. Though this storage process reduces the memory constraints of the user’s computer, the time deadline is a serious concern. In this paper, Adaptive Cost-based Task Scheduling (ACTS) is proposed to provide data access to the virtual machines (VMs) within the deadline without increasing the cost. ACTS considers the data access completion time for selecting the cost effective path to access the data. To allocate data access paths, the data access completion time is computed by considering the mean and variance of the network service time and the arrival rate of network input/output requests. Then the task priority is assigned to the removed tasks based data access time. Finally, the cost of data paths are analyzed and allocated based on the task priority. Minimum cost path is allocated to the low priority tasks and fast access path are allocated to high priority tasks as to meet the time deadline. Thus efficient task scheduling can be achieved by using ACTS. The experimental results conducted in terms of execution time, computation cost, communication cost, bandwidth, and CPU utilization prove that the proposed algorithm provides better performance than the state-of-the-art methods.

This paper points towards the integration rules of mapping from relational database such as SQL DDL, schema and data into the semantic web ontology such as OWL which build on top of RDF using vocabulary RDFS and XML schema data type. From the study, different cases are considered: such as tables, relationships, composite-column, single or multiple primary key, foreign key, constraints, check between, check like, etc, and then using one of these cases to mapping data rows to ontology instances. This approach reduces the cost and time for building ontology (OWL).