William Wei Song

Trust Based Service Selection in Service Oriented Environment

In a service-oriented environment, it is inevitable and indeed quite common to deal with web services, whose reliability is unknown to the users. The reputation system is a popular technique currently used for providing a global quality score of a service provider to requesters. However, such global information is far from sufficient for service requesters to choose the most qualified services. In order to tackle this problem, the authors present a trust based architecture containing a computational trust model for quantifying and comparing the trustworthiness of services. In this trust model, they firstly construct a network based on the direct trust relations between participants and rating similarity in service oriented environments, then propose an algorithm for propagating trust in the social network based environment which can produce personalized trust information for a specific service requester, and finally implement the trust model and simulate various malicious behaviors in not only dense but also sparse networks which can verify the attack-resistant and robustness of the proposed approach. The experiment results also demonstrate the feasibility and benefit of the approach.

A Recommender System Model Combining Trust with Topic Maps

Recommender Systems (RS) aim to suggest users with items that they might like based on users’ opinion on items. In practice, information about the users’ opinion on items is usually sparse compared to the vast information about users and items. Therefore it is hard to analyze and justify users’ favorites, particularly those of cold start users. In this paper, we propose a trust model based on the user trust network, which is composed of the trust relationships among users. We also introduce the widely used conceptual model Topic Map, with which we try to classify items into topics for Recommender analysis. We novelly combine trust relations among users with Topic Maps to resolve the sparsity problem and cold start problem. The evaluation shows our model and method can achieve a good recommendation effect.

A Computational Model for Trust-Based Collaborative Filtering

The inherent weakness of the data on user ratings collected from the web, such as sparsity and cold-start, has limited the data analysis capability and prediction accuracy in recommender systems. To alleviate this problem, trust has been incorporated in collaborative filtering approaches with encouraging experimental results. In this paper, we propose a computational model for trust-based CF with three different methods to infer trust in a social network, based on a detailed data analysis of hotel dataset. We apply these methods on users ratings of hotels and show its feasibility by comparing the testing results with conventional CF algorithm using evaluation metrics Mean absolute error (MAE) and prediction coverage. Our experimental results indicate that the use of trust can improve prediction accuracy if the definition of trust is reasonable enough.

LDA-TM: A two-step approach to Twitter topic data clustering

The Twitter System is the biggest social network in the world, and everyday millions of tweets are posted and talked about, expressing various views and opinions. A large variety of research activities have been conducted to study how the opinions can be clustered and analyzed, so that some tendencies can be uncovered. Due to the inherent weaknesses of the tweets - very short texts and very informal styles of writing - it is rather hard to make an investigation of tweet data analysis giving results with good performance and accuracy. In this paper, we intend to attack the problem from another aspect - using a two-layer structure to analyze the twitter data: LDA with topic map modelling. The experimental results demonstrate that this approach shows a progress in twitter data analysis. However, more experiments with this method are expected in order to ensure that the accurate analytic results can be maintained.

Community-Based Message Opportunistic Transmission

A Mobile Social Networks (MSN) is a kind of opportunistic network, which is composed of numerous mobile nodes with social characteristic. By now, the prevalent community-based routing algorithms mainly choose the optimal social characteristic node to forward messages, however they rarely consider the effects of community distribution on mobile nodes and time-varying characteristics of network. These algorithms usually lead to a high consumption of network resources and a low successful delivery ratio if they are used directly in mobile social networks. In order to solve this problem, we build a time-varying community-based network model, and propose a community-aware message opportunistic transmission algorithm (CMOT) in this paper. For inter-community messages transmission, the CMOT chooses an optimal community path by comparing the community transmission probability. In local communities, messages are forwarded according to the encounter probability between nodes. The simulation results show that, in comparison with classical routing algorithms, such as PRoPHET, MaxProp, Spray and Wait, and CMTS, the CMOT can improve the successful delivery ratio of messages and reduce network overhead obviously.

Combination of Evaluation Methods for Assessing the Quality of Service for Express Delivery Industry

In the view of the current service development of the express delivery industry and the data quality problem experienced thereof, we consider to construct an index-based evaluation system for the service quality for the express delivery industry through the analysis of market investigation and data analysis. This system applies analytic hierarchy process (AHP), to survey expert’s options and obtain the index weights. The analytical evaluation of service quality for the specific express delivery company is conducted with the fuzzy comprehensive evaluation method. A service satisfaction degree for the express delivery company is generated to improve the overall service performance. Through evaluating results of the solution to the problems in the quality of service, this paper aims at establishing a guideline to improve their service quality for express delivery enterprises. This research aims at the development of a novel method for service quality evaluation in the area of the fast growing businesses of express delivery enterprises.

A Structural Analysis of SLAs and Dependencies Using Conceptual Modelling Approach

Making good use of Service Level Agreements (SLA) becomes crucial for an enterprise both to provide value added products and services to customers and to protect the interest of parties involved in the business activities. Well formed and effective structural representation and management of SLAs in conceptual modeling can greatly support the understanding and communication of service development and deployment as well as maintenance of quality of service. Existing specifications and structures for SLAs do not fully formalize and support for different automatic and dynamic behavioral aspects needed within business enterprises due to lack of study focusing on SLAs templates and their contents, which are mostly written on Natural Language (NL). We address the issues of how to use conceptual models to describe the structures of SLAs and the various relationships between SLAs and their items, and hence to better depict business domains. With focus on the contents, process, and dependencies among SLAs, we aim to use so generated concept model for service discovery, service delivery and scheduling.

A Multi-Semantic Classification Model of Reviews Based on Directed Weighted Graph

Semantic and sentimental analysis plays an important role in natural language processing, especially in textual analysis, and has a wide range of applications in web information processing and management. This paper intends to present a sentimental analysis framework based on the directed weighted graph method, which is used for semantic classification of the textual comments, i.e. user reviews, collected from the e-commerce websites. The directed weighted graph defines a formal semantics lexical as a semantic body, denoted to be a node in the graph. The directed links in the graph, representing the relationships between the nodes, are used to connect nodes to each other with their weights. Then a directed weighted graph is constructed with semantic nodes and their interrelationships relations. The experimental results show that the method proposed in the paper can classify the semantics into different classification based on the computation of the path lengths with a threshold.

Community-Based Message Transmission with Energy Efficient in Opportunistic Networks

An Opportunistic Networks is a wireless self-organized network, in which there is no need to build a fixed connectivity between source node and destination node, and the communication depends on the opportunity of node meeting. There are some classical message transmission algorithms, such as PRoPHET, MaxProp, and so on. In the Opportunity Networks with community characteristic, the different message transmission strategies can be sued in inter-community and intra-community. It improves the message successful delivery ratio significantly. The classical algorithms are CMTS and CMOT. We propose an energy efficient message forwarding algorithm EEMF for community-based Opportunistic Networks in this paper. When a message is transmitted, we consider not only the community characteristic, but also the residual energy of each node. The simulation results show that the EEMF algorithm can improve the message successful delivery ratio and reduce the network overhead obviously, in comparison with classical routing algorithms, such as PRoPHET, MaxProp, CMTS and CMOT. Meanwhile the EEMF algorithm can reduce the nodes energy consumption and prolong the lifetime of network.

An e-Curriculum Based Systematic Resource Integration Approach to Web-Based Education

With the rapid advancement of the web technology, more and more educational resources, including software applications for teaching/learning methods, are available across the web, which enables lea ...