Next Point-of-Interest (POI) recommendation plays an important role in location-based services. The state-of-the-art methods utilize recurrent neural networks (RNNs) to model users' check-in sequences and have shown promising results. However, they tend to recommend POIs similar to those that the user has often visited. As a result, users become bored with obvious recommendations. To address this issue, we propose Serendipity-oriented Next POI Recommendation model (SNPR), a supervised multi-task learning problem, with objective to recommend unexpected and relevant POIs only. To this end, we define the quantitativeserendipity as a trade-off ofrelevance andunexpectedness in the context of next POI recommendation, and design a dedicated neural network with Transformer to capture complex interdependencies between POIs in user's check-in sequence. Extensive experimental results show that our model can improverelevance significantly while theunexpectedness outperforms the state-of-the-art serendipity-oriented recommendation methods.

Edge data caching has attracted tremendous attention in recent years. Service providers can consider caching data on nearby locations to provide service for their app users with relatively low latency. The key to enhance the user experience is appropriately choose to cache data on the suitable edge servers to achieve the service providers' objective, e.g., minimizing data retrieval latency and minimizing data caching cost, etc. However, Quality of Experience (QoE), which impacts service providers' caching benefit significantly, has not been adequately considered in existing studies of edge data caching. This is not a trivial issue because QoE and Quality-of-Service (QoS) are not correlated linearly. It significantly complicates the formulation of cost-effective edge data caching strategies under the caching budget, limiting the number of cache spaces to hire on edge servers. We consider this problem of QoE-aware edge data caching in this paper, intending to optimize users' overall QoE under the caching budget. We first build the optimization model and prove the NP-completeness about this problem. We propose a heuristic approach and prove its approximation ratio theoretically to solve the problem of large-scale scenarios efficiently. We have done extensive experiments to demonstrate that the MPSG algorithm we propose outperforms state-of-the-art approaches by at least 68.77%.

With the rapid increase in the use of mobile devices in people's daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed around mobile users, caching popular data on edge servers can ensure mobile users' fast access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data cache problem with a focus on the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we attack the data caching problem in the edge computing environment from the service providers' perspective, who would like to maximize their venues of caching their data. This problem is complicated because data caching produces benefits at a cost and there usually is a trade-off in-between. In this paper, we formulate the data caching problem as an integer programming problem, and maximizes the revenue of the service provider while satisfying a constraint for data access latency. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches.

MSN (Mobile Social Network) enables nodes (mobile devices) to realize packet delivery by leveraging social relationships of mobile users. However, MSN has to adapt with the daily increasing content (e.g., video) requirement requested by mobile users. Based on the fact that ICN (Information-Centric Networking) supports mobility naturally, we propose an MSN content-centric routing scheme. It is inspired by the in-network caching and named-content properties of Named Data Networking. By the analysis of the historical requested content names of MSN users, a novel interest distance metric is proposed, based on which, the forwarding node is selected for an interest packet. Meanwhile, by the analysis of the historical encounters of MSN nodes, a novel encounter regularity metric is proposed, based on which, the forwarding node is selected for a data packet. Furthermore, in order to respond the forth-coming requests rapidly, nodes preferentially cache the content which with high requested probability. By comparing with the existed schemes, simulation experiments represent that our scheme is effective and feasible.

Software Defined Networking (SDN) decouples the control plane from the data plane, which increases network scalability and flexibility. But malicious applications on SDN controller can cause the entire network to crash. So, we design a reputation and incentive mechanism on SDN to reduce application's malicious access. In the proposed module, first of all, the application behavior is analyzed and the malicious accesses are identified, which are used to build the reputation and incentive mechanism. Second, the analysis results of the application behavior are combined through beta probability density to obtain the reputation rating. The reward or punishment will be given based on the behavior and reputation of the application under the selected social strategy. Simulation results show that the system can accurately identify malicious behavior and reduce malicious requests, with an acceptable runtime overhead about 300 microseconds.

Software-defined satellite networks (SDSN) move the control logic off the forwarding devices and into the external controller, which achieves network flexibility, programmability, evolvability, and visibility. The controller placement in SDSN, as the foundation of SDSN implementation, has been seldom studied. In this paper, we proposed a three-layer hierarchical controller architecture for software-defined geostationary earth orbit/low earth orbit (GEO/LEO) satellite networks. Specifically, network operations control center (NOCC) is deployed as super controller, GEO satellites are domain controllers, and a part of LEO satellites are slave controllers. Based on this architecture, we further proposed a slave controller selection strategy to facilitate cost reduction and stability enhancement. The feasibility of this controller architecture is validated in terms of the maximum switch to controller and controller to controller delays. Besides, the influences of service request distribution on these two control delays are analyzed.

SUMMARY Composite service selection is one of the core research issues in Web service composition. Because of the complex service correlation context, candidate services may perform differently when being used with other services. Presently, most service selection approaches ignore this issue, which makes the selected composite services less efficient than expected. To solve this problem, a service correlation context‐aware composite service selection approach is proposed on the basis of the concept of single‐entry single‐exit (SESE) region. The general process of our approach is as follows: (1) mining the SESE patterns that are frequently used together in the set of efficiently executed instances of a composite service; (2) dividing the process model of the composite service into SESE regions and generating the candidate SESE pattern set of each region, using the discovered SESE pattern set; and (3) optimizing composite service selection globally on the basis of QoS using divided regions as selection units and their candidate pattern sets as candidate service sets. Because SESE patterns are testified by large amount of efficiently executed instances, they have higher quality than the results of independent selection of services in an SESE region. Experimental results demonstrated that our approach can improve the quality of selected composite services effectively in the correlation context. Concurrency and Computation: Practice and Experience, 2012.© 2013 Wiley Periodicals, Inc.

With the increasing numbers of Web services and service users on World Wide Web, predicting QoS (Quality of Service) for users will greatly aid service selection and discovery. Due to the different backgrounds and experiences of users, they have different QoS experiences when interacting with the same service. Even two users who have similar experiences on some services can have diverging views when considering services. This paper proposes an approach to predict QoS based on other users' QoS experiences. This method employs similarity mining and prediction from users' experience by firstly selecting a set of web services that have the highest degree of similarity with the target service by comparing the target service with the others services used by target user. Secondly, the missing value can be calculated through the data of similar services. On the basis of that, we calculate the user similarity and predict QoS data for target user. Experimental results show that it can improve the prediction accuracy of QoS for Web service by using this method.

Community structure has been recognized as an important statistical feature of network systems over the past decade. The web service in SOA system naturally forms into some service community during execution process, within which the links between nodes are very dense, but between which they are quite sparse. These service communities were generated by repeatedly interaction between composite services which accomplish the same task. Mining and analysis web service community will help design SOA system and predict service behavior. This article addresses the problem of how to discovering and quantifying web services community formed by closely interactive web services and gives the composite web service discovery technique. We consider the case where the details usage record is logging by execution engine. We proposed a novel approach which construct web service interactive network (WSIN) from usage log and get community structure by spectrum clustering. Generally, the web services belong to same cluster have strong relative to same task object and we call it web service community. The approach has been implemented in an experience system for web services dynamic composition and discovery, and the experimental results demonstrated the efficiency and effectiveness of the proposed algorithm.

Traditional Chinese medicine is special for Western people. Its diagnosis and treatment depend on syndrome. Syndrome is composed of some symptoms, and each symptom demonstrates different values in different syndrome. In this paper, we first describe approach of syndrome and symptom in TCM. Then, a hierarch model of syndrome differentiation in traditional Chinese medicine is proposed. According to the model, data mining model is designed to complete it. Given special data mining schema and character of high dimensional data sets, we introduce hypergraph in cluster and attributes combination in association procedure. Finally, the result of model in syndrome differentiation of traditional Chinese medicine is given