Probabilistic approach for QoS-aware recommender system for trustworthy web service selection

Cloud service recommendation for small and medium-sized enterprises: A context-aware group decision making approach

Recently, the enormous advantages of cloud services make them increasingly appealing to the small and medium-sized enterprises. The growing number of available services makes it challenging to select trustworthy services. Existing approaches focus on user preferences to guide personalized services recommendation for individual users, but lack of the research on trustworthy service recommendation for the small and medium-sized enterprises that represents a group user consisting of multiple individual users. For this type of enterprise, the cloud services recommendation must address the challenges from the diverse client context of individual users, the imprecise quality of experience in an uncertain cloud environment and the invalid or unsatisfactory recommendations. A client context-aware approach is proposed to recommend trustworthy cloud services for the small and medium-sized enterprises based on non-compensatory multi-criteria decision-making. In it, a type of client context is viewed as an independent evaluation criterion, and the interval neutrosophic numbers are employed to measure the fuzzy trustworthiness of cloud services. Based on the investigated outranking relations of interval neutrosophic numbers, a non-compensatory multi-criteria decision-making procedure via an improved ELECTRE III method is developed to rank candidate services. Experimental results demonstrate that this approach could efficiently produces the accurate ranking results of cloud services and effectively recommend the trustworthy service for small and medium-sized enterprises.

Data Quality and Trust: Review of Challenges and Opportunities for Data Sharing in IoT

Existing research recognizes the critical role of quality data in the current big-data and Internet of Things (IoT) era. Quality data has a direct impact on model results and hence business decisions. The growth in the number of IoT-connected devices makes it hard to access data quality using traditional assessments methods. This is exacerbated by the need to share data across different IoT domains as it increases the heterogeneity of the data. Data-shared IoT defines a new perspective of IoT applications which benefit from sharing data among different domains of IoT to create new use-case applications. For example, sharing data between smart transport and smart industry can lead to other use-case applications such as intelligent logistics management and warehouse management. The benefits of such applications, however, can only be achieved if the shared data is of acceptable quality. There are three main practices in data quality (DQ) determination approaches that are restricting their effective use in data-shared platforms: (1) most DQ techniques validate test data against a known quantity considered to be a reference; a gold reference. (2) narrow sets of static metrics are used to describe the quality. Each consumer uses these metrics in similar ways. (3) data quality is evaluated in isolated stages throughout the processing pipeline. Data-shared IoT presents unique challenges; (1) each application and use-case in shared IoT has a unique description of data quality and requires a different set of metrics. This leads to an extensive list of DQ dimensions which are difficult to implement in real-world applications. (2) most data in IoT scenarios does not have a gold reference. (3) factors endangering DQ in shared IoT exist throughout the entire big-data model from data collection to data visualization, and data use. This paper aims to describe data-shared IoT and shared data pools while highlighting the importance of sharing quality data across various domains. The article examines how we can use trust as a measure of quality in data-shared IoT. We conclude that researchers can combine such trust-based techniques with blockchain for secure end-to-end data quality assessment.