A Review on Framework and Quality of Service Based Web Services Discovery

Selection of Web services (WSs) is one of the most important steps in the application of different types of WSs such as WS composition systems and the Universal Description, Discovery, and Integration (UDDI) registries. The more available these WSs on the Internet are, the wider the number of these services whose functions match the various service requests is. Selecting WSs with higher quality largely depends on the quality of service (QoS) since it plays a significant role in selecting such services. In achieving this selection of the best WSs, the potential WSs are ranked according to the user’s necessities on service quality. In many cases, the value of QoS ontology is realized by its support for nonfunctional features of WSs. This ontology is also capable of providing solutions to the interoperability of QoS description. Moreover, based on the QoS ontology, it becomes more possible to develop a framework of semantic WS discovery. The framework enhances the automatic discovery of WSs and can improve the users’ efficiency in finding the best web services. Thus, Web Services are software functionalities publish and accessible through the Internet. Different protocols and web mechanism have been defined to access these Services

OWL-Q for Semantic QoS-based Web Service Description and Discovery

Abstract. Semantic Web Services are emerging for their promise to produce a more accurate and precise Web Service discovery process. However, most of research approaches focus only on the functional part of semantic Web Service description. The above fact along with the proliferation of Web Services is highly probable to lead to a situation where Web Service registries will return many functionally-equivalent Web Service advertisements for each user request. This problem can be solved with the semantic description of QoS for Web Services. QoS is a set of non-functional properties encompassing performance and networkrelated characteristics of resources. So it can be used for distinguishing between functionally-equivalent Web Services. Current research approaches for QoS-based Web Service description are either syntactic or poor or non-extensible. To solve this problem, we have developed a rich and extensible ontological specification called OWL-Q for semantic QoSbased Web Service description. We analyze all OWL-Q parts and reason that rules should be added in order to support property inferencing and constraint enforcement. Finally, we line out our under-development semantic framework for QoS-based Web Service description and discovery.

An architecture for user preference-based IoT service selection in cloud computing using mobile devices for smart campus

The Internet of things refers to the set of objects that have identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social environments and user context. Interconnected devices communicating to each other or to other machines on the network have increased the number of services. The concepts of discovery, brokerage, selection and reliability are important in dynamic environments. These concepts have emerged as an important field distinguished from conventional distributed computing by its focus on large-scale resource sharing, delivery and innovative applications. The usage of Internet of Things technology across different service provisioning environments has increased the challenges associated with service selection and discovery. Although a set of terms can be used to express requirements for the desired service, a more detailed and specific user interface would make it easy for the users to express their requirements using high-level constructs. In order to address the challenge of service selection and discovery, we developed an architecture that enables a representation of user preferences and manipulates relevant descriptions of available services. To ensure that the key components of the architecture work, algorithms (content-based and collaborative filtering) derived from the architecture were proposed. The architecture was tested by selecting services using content-based as well as collaborative algorithms. The performances of the algorithms were evaluated using response time. Their effectiveness was evaluated using recall and precision. The results showed that the content-based recommender system is more effective than the collaborative filtering recommender system. Furthermore, the results showed that the content-based technique is more time-efficient than the collaborative filtering technique