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Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The integration of coherent services plays a potential role in the field of Service Oriented Applications. Achieving this potential standard crucially depends on the ability to recognize and exploit the available services based on user requirements. In general, the user preferences on Quality of Service (QoS) requirements are fuzzy in nature. In addition to that, the QoS requirements are user dependent even if the functional requirements are the same. With a large number of available services, service selection for dynamic composition at run time is a challenge. Functional and non-functional assumptions made at design time may violate at run-time. These violations require run time reaction, by adopting a run-time process. Therefore, dynamic and fuzzy QoS-aware service discovery for run-time composition and continuous adaptation is a strong requirement in service oriented computing. Considering that different users follow different fuzzy reasoning in various contexts at different times, a fuzzy inference based service selection approach has been proposed in this paper. Continuous adaptation is done, as and when a design time assumption violation is reported by a run-time monitoring system. We have implemented and tested the proposed approach and the results show its effectiveness. [ABSTRACT FROM AUTHOR]

The emergence of Internet of Things (IoT) integrates the cyberspace with the physical space. With the rapid development of IoT, large amounts of IoT services are provided by various IoT middleware solutions. So, discovery and selecting the adequate services becomes a time-consuming and challenging task. This paper proposes a novel similarity-measurement for computing the similarity between services and introduces a new personalized recommendation approach for real-world service based on collaborative filtering. In order to evaluate the performance of proposed recommendation approach, large-scale of experiments are conducted, which involves the QoS-records of 339 users and 5825 real web-services. The experiments results indicate that the proposed approach outperforms other compared approaches in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Abstract: With the growing interests in semantic web services and context-aware computing, the importance of ontologies, which enable us to perform context-aware reasoning, has been accepted widely. While domain-specific and general-purpose ontologies have been developed, few attempts have been made for a situation ontology that can be employed directly to support activity-oriented context-aware services. In this paper, we propose an approach to automatically constructing a large-scale situation ontology by mining large-scale web resources, eHow and wikiHow, which contain an enormous amount of how-to instructions (e.g., “How to install a car amplifier”). The construction process is guided by a situation model derived from the procedural knowledge available in the web resources. Two major steps involved are: (1) action mining that extracts pairs of a verb and its ingredient (i.e., objects, location, and time) from individual instructional steps (e.g., ) and forms goal-oriented situation cases using the results and (2) normalization and integration of situation cases to form the situation ontology. For validation, we measure accuracy of the action mining method and show how our situation ontology compares in terms of coverage with existing large-scale ontology-like resources constructed manually. Furthermore, we show how it can be utilized for two applications: service recommendation and service composition. [Copyright &y& Elsevier]

As a new computing technology proposed with the development of 5G, IoT technologies and increasing requirement of mobile applications and services, edge computing enables mobile application developers and content providers to serve context-aware mobile services (e.g., mobile app recommendation). Mobile app recommendation is known as an effective solution to overcome the information overload in mobile app markets. Most existing models only consider user-app interaction and feature modeling, and neglect the structural information which actually is a crucial part in the scenario of app recommendation. To fully exploit both structural and feature information for app recommendation, this paper proposes a novel heterogeneous graph neural network framework (HGNRec) including one inner module and one outer module. Specifically, the inner module is able to use a node-level attention to learn the importance between a node and its meta-path based neighbors. The outer module with a path-level attention can learn the importance of different meta-paths. With the learned importance from two modules, the comprehensive embeddings for user and app nodes can be generated by integrating features from meta-path based neighbors. Extensive experiments on the real-world Google Play mobile app dataset demonstrate the effectiveness of HGNRec. • HGNRec is the first attempt to model mobile app recommendation under the heterogeneous graph neural network framework. • Hierarchical attention layers can obtain the significances of different neighbor nodes and meta-paths. • HGNRec performs best when both accuracy and efficiency are considered. • Structural information with more heterogeneous relationships leads a superior performance. [ABSTRACT FROM AUTHOR]