Internet of Things has become a common paradigm for various domains. More and more smart services are connected to the internet by means of diverse intelligent edge devices. While people enjoy these convenient services, IoT service system will produce mass IoT data, which can be analyzed to improve service quality. In addition, these services usually coexist, which may lead to conflicts. Moreover, each user may have different kinds of needs. Suppose that in a smart home there are more than one person, a conflict may occur when they request the same service. We propose an Intelligent Hybrid System (IHS) to resolve IoT service conflicts, which consists of two steps, i.e. calculating historical preference mean and averaging on-site demand preferences. The first step is achieved by a Weighted Voting Game (WVG) model, where machine learning is used to process historical IoT data and learn associated parameters. The second step is based on Analytic Hierarchy Process (AHP) method, where some residents contextual factors are considered. Based on the system, we have launched a series of experiments. Experiment results based on real datasets demonstrate the efficacy of the proposed method in term of key accuracy metric, thereby validating our theoretical motivations and benefits.

Abstract Crossover services involve deep convergence of services in different domains. Requirements analysis of crossover services often requires the collaboration of engineers in different domains and various organizations. The industry has a demand for sophisticated requirements engineering (RE) methods to address challenges brought by crossover and convergence. However, many methods proposed by researchers have not been widely used in the industry, and the reason is that these methods provide technological solutions to the problems and challenges that may not be considered accurate or even real by practitioners in the crossover services industry. A great effort is needed to explore what needs, expectations, and constraints that RE approaches must satisfy and promote the adoption of novel RE approaches in the crossover services industry. The authors have conducted an industrial study to gain an in‐depth understanding of practitioners' needs concerning RE research and method development. The study involved qualitative interviews as well as questionnaires to collect data. The objective of this study is to investigate issues, challenges, and practices of crossover services RE from practitioners' perspectives and to reveal potential guidelines for researchers and tools developers. Findings from five aspects of RE methods are reported and some research directions for future crossover services RE research are provided.

Quality of Service (QoS) has been widely used for personalized Web service recommendation. Since QoS information usually cannot be predetermined, how to make personalized QoS prediction precisely becomes a challenge of Web service recommendation. Time series forecasting and collaborative filtering are two mainstream technologies for QoS prediction. However, on one hand, existing time series forecasting approaches based on Auto Regressive Integrated Moving Average (ARIMA) models do not take the latest observation as a feedback to revise forecasts. Moreover, they only focus on predicting future QoS values for each individual Web service. Service users' personalized factors are not taken into account. On the other hand, collaborative filtering facilitates user-side personalized QoS evaluation, but rarely precisely models the temporal dynamics of QoS values. To address the limitations of existing QoS prediction methods, this paper proposes a novel personalized QoS prediction approach considering both the temporal dynamics of QoS attributes and the personalized factors of service users. Our approach seamlessly combines collaborative filtering with improved time series forecasting which uses Kalman filtering to compensate for shortcomings of ARIMA models. Finally, the experimental results show that the proposed approach can improve the accuracy of personalized QoS prediction significantly.

Service computing is a popular development paradigm in information technology. The functional properties of Web services assure correct functionality of cloud applications, while the nonfunctional properties such as reliability might significantly influence the user-perceived availability evaluation. Reliability rankings provide valuable information for making optimal cloud service selection from a set of functionally-equivalent candidate services. There existed several approaches that can conduct reliability ranking prediction for Web services. Those approaches acquire different rankings with different preference functions. It is arduous to determine whether there exists the best one in them, and what is the best one if not. This paper proposes a learning approach to reliability ranking prediction for Web services which utilizes past service invocation logs to train preference function. To validate the proposed approach, large-scale experiments are conducted based on a real-world Web service dataset, WSDream. The results show that our proposed approach achieves higher prediction accuracy than the existing approaches.

With the incessant growth of web services on the Internet, how to design effective web service recommendation technologies based on Quality of Service (QoS) is becoming more and more important. Web service recommendation can relieve users from tough work on service selection and improve the efficiency of developing service-oriented applications. Neighborhood-based collaborative filtering has been widely used for web service recommendation, in which similarity measurement and QoS prediction are two key issues. However, traditional similarity models and QoS prediction methods rarely consider the influence of time information, which is an important factor affecting the QoS performance of web services. Furthermore, it is difficult for the existing similarity models to capture the actual relationships between users or services due to data sparsity. The two shortcomings seriously devalue the performance of neighborhood-based collaborative filtering. In this paper, the authors propose an improved time-aware collaborative filtering approach for high-quality web service recommendation. Our approach integrates time information into both similarity measurement and QoS prediction. Additionally, in order to alleviate the data sparsity problem, a hybrid personalized random walk algorithm is designed to infer indirect user similarities and service similarities. Finally, a series of experiments are provided to validate the effectiveness of our approach.

Recently, there are many improvements in Internet of Things (IOT). Through the recombination and optimization, the real-world devices can provide their functionality as web services in IOT. However, it is very difficult to cost-effectively access to the Internet of Things due to the environmental changes. In this paper, firstly, a Petri net-based model for service composition in IOT is proposed, which uses a comprehensive performance function rtc (involving reliability, response time and cost) to evaluate the cost-effectiveness. Then, the Find to Optimal algorithm is addressed to find a cost-effective composition path. Furthermore, when environments change dynamically, the FBased Monitor algorithm can well solve the composition. Finally, the experiments prove the soundness and correctness of our model and algorithms.

Recent years have witnessed a rapid growth in using Web services for data publishing and sharing among organizations. To improve the efficiency of software development and economize on human and material resources, service reuse is viewed as a powerful means which will not only reuse atomic services, but also reuse arbitrary granularities of Service Process Fragments (SPFs). However, effectively reusing arbitrary granularities of SPFs has not been solved yet. In this paper, we propose a novel method of SPF reuse, named SCKY, based on the Cocke-Kasami-Younger (CKY) algorithm. We first present an extended CKY to do SPF-query. Then we address how to do SPF-query by a probability CKY, i.e. return a SPF with maximum emergence probability. Finally, we explore the QoS Query of SPF. Through a set of experiments, the effectiveness and robustness of our approach are evaluated, where the dataset is constructed by the Web Service Challenge Testset Generator1 (CTG).