Ontological approach for database integration

Database integration is one of the research areas that have gained a lot of attention from researcher. It has the goal of representing the data from different database sources in one unified form. To reach database integration we have to face two obstacles. The first one is the distribution of data, and the second is the heterogeneity. The Web ensures addressing the distribution problem, and for the case of heterogeneity there are many approaches that can be used to solve the database integration problem, such as data warehouse and federated databases. The problem in these two approaches is the lack of semantics. Therefore, our approach exploits the Semantic Web methodology. The hybrid ontology method can be facilitated in solving the database integration problem. In this method two elements are available; the source (database) and the domain ontology, however, the local ontology is missing. In fact, to ensure the success of this method the local ontologies should be produced. Our approach obtains the semantics from the logical model of database to generate local ontology. Then, the validation and the enhancement can be acquired from the semantics obtained from the conceptual model of the database. Now, our approach can be applied in the generation phase and the validation-enrichment phase. In the generation phase in our approach, we utilise the reverse engineering techniques in order to catch the semantics hidden in the SQL language. Then, the approach reproduces the logical model of the database. Finally, our transformation system will be applied to generate an ontology. In our transformation system, all the concepts of classes, relationships and axioms will be generated. Firstly, the process of class creation contains many rules participating together to produce classes. Our unique rules succeeded in solving problems such as fragmentation and hierarchy. Also, our rules eliminate the superfluous classes of multi-valued attribute relation as well as taking care of neglected cases such as: relationships with additional attributes. The final class creation rule is for generic relation cases. The rules of the relationship between concepts are generated with eliminating the relationships between integrated concepts. Finally, there are many rules that consider the relationship and the attributes constraints which should be transformed to axioms in the ontological model. The formal rules of our approach are domain independent; also, it produces a generic ontology that is not restricted to a specific ontology language. The rules consider the gap between the database model and the ontological model. Therefore, some database constructs would not have an equivalent in the ontological model. The second phase consists of the validation and the enrichment processes. The best way to validate the transformation result is to facilitate the semantics obtained from the conceptual model of the database. In the validation phase, the domain expert captures the missing or the superfluous concepts (classes or relationships). In the enrichment phase, the generalisation method can be applied to classes that share common attributes. Also, the concepts of complex or composite attributes can be represented as classes. We implement the transformation system by a tool called SQL2OWL in order to show the correctness and the functionally of our approach. The evaluation of our system showed the success of our proposed approach. The evaluation goes through many techniques. Firstly, a comparative study is held between the results produced by our approach and the similar approaches. The second evaluation technique is the weighting score system which specify the criteria that affect the transformation system. The final evaluation technique is the score scheme. We consider the quality of the transformation system by applying the compliance measure in order to show the strength of our approach compared to the existing approaches. Finally the measures of success that our approach considered are the system scalability and the completeness

Cooperative Volunteer Protocol to Detect Non-Line of Sight Nodes in Vehicular Ad hoc Networks

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. CTIA vehicular Ad hoc Network (VANET) is a special type of Mobile Ad hoc Network (MANET) application that impacts wireless communications and Intelligent Transport Systems (ITSs). VANETs are employed to develop safety applications for vehicles to create a safer and less cluttered environment on the road. The many remaining challenges relating to VANETs have encouraged researchers to conduct further investigation in this field to meet these challenges. For example, issues pertaining to routing protocols, such as the delivery of warning messages to vehicles facing Non-Line of Sight (NLOS) situations without causing a broadcasting storm and channel contention are regarded as a serious dilemma, especially in congested environments. This prompted the design of an efficient mechanism for a routing protocol capable of broadcasting warning messages from emergency vehicles to vehicles under NLOS conditions to reduce the overhead and increase the packet delivery ratio with reduced time delay and channel utilisation. This work used the cooperative approach to develop the routing protocol named the Co-operative Volunteer Protocol (CVP), which uses volunteer vehicles to disseminate the warning message from the source to the target vehicle experiencing an NLOS situation. A novel architecture has been developed by utilising the concept of a Context-Aware System (CAS), which clarifies the OBU components and their interaction with each other to collect data and make decisions based on the sensed circumstances. The simulation results showed that the proposed protocol outperformed the GRANT protocol with regard to several metrics such as packet delivery ratio, neighbourhood awareness, channel utilisation, overhead, and latency. The results also showed that the proposed CVP could successfully detect NLOS situations and solve them effectively and efficiently for both the intersection scenario in urban areas and the highway scenario

Generating OWL ontology for database integration.

Abstract-Today, databases provide the best technique for storing and retrieving data, but they suffer from the absence of a semantic perspective, which is needed to reach global goals such as the semantic web and data integration. Using ontologies will solve this problem by enriching databases semantically. Since building an ontology from scratch is a very complicated task, we propose an automatic transformation system to build Web Ontology Language OWL ontologies from a relational model written in Structured Query Language SQL. Our system also uses metadata, which helps to extract some semantic aspects which could not be inferred from the SQL. Our system analyzes database tuples to capture these metadata. Finally, the outcome ontology of the system is validated manually by comparing it with a conceptual model of the database (E/R diagram) in order to obtain the optimal ontology

Security Management Techniques Designed for Mobile Ad Hoc Network of Networks (MANoN)

The aim of wireless technology is to meet the need for fast, reliable and secure information exchange. Based on collaboration between individual network nodes, the emerging Mobile Ad hoc Network of Networks (MANoN) technology attempts to offer users with anytime and anywhere services in a large heterogeneous infrastructure-less wireless network. The unique characteristics of MANoN makes such networks highly vulnerable to security attacks compared with wired networks or even normal Mobile Ad hoc Networks (MANET). In any system, providing any components related to security management is an issue; consequently, dealing with an infrastructure-less MANoN will be more challenging. In this article, a novel security management system is proposed based upon recommendation of ITU-T M.3400, which is used to evaluate, report on the behaviour of our MANoN nodes and support the complex services our system might need to accomplish. In addition, we concentrate on providing the essential components (Prevention and Detection) to satisfy the objectives of security requirements (e.g., authentication and authorisation). We will also provide results related to the main metrics used to evaluate the proposed security mechanisms