Methodology And Key Technology For Constructing Personalized And Intelligent M-commerce System

With the rapid growth of Internet and its related technology over the last two decades, it makes significant change about every important realm of our modern life. One important variation of our daily lives is the way of conducting business. Clearly, Electronic commerce (E-commerce) is the most typical sample of internet applications. Meanwhile, At advent of the wireless network, mobile computing and their related technologies, mobile internet gradually becomes the important variation in landscape of mobile Information ages. M-commerce is an attractive research area due to its relative novelty, mobility, personality, and flexibility, and great potential in business applications.In comparison to desktop-based computing paradigm, M-commerce has some limitations due to physical constraints of mobile handheld devices, such as small screen size, poor network connectivity, low limited battery capacity, limited storage and computing capacity. On one hand, it is expensive and unreliable when an amount of data has to be transferred between the client and the remote server. On the other hand, it is impossible to retain a long-time connectivity to maintain face-to-face communication between mobile consumers and venders. In addition, the nomadic users need to frequently check trading opportunity, as well as carry out fuzzy and complex information exchange and decision-making tasks. Therefore, it leads to the raise of revenue and creates the risk of missing trade opportunities if the trade time is constrained by the limited availability of physical access to the service. It is well-known that an M-commerce transaction involves a sequence of activities, such as negotiation, purchasing, shipment, payment and logistic services. Existing M-commerce applications are lack of fully automated business processes and still require significant manual effort.In this paper we present a general solution of integrating Web Service, Mobile Agent, Context-aware Computing and workflow technology to implement automated trading task and compose services dynamically in real time to create a highly personalized assistant.Firstly, In order to get benefits of both Web Services and agent paradigm, we present a common Service Description Framework, which clearly presents the function and computing capability of concrete service. Furthermore, we define a comprehensive QoS Model to present the no-functional attributes of service. We bring forward the concept of Abstract Service (AS) and Abstract Business Process Logic (ABPL). Abstract Service is a high-level description of the capabilities and categorization of an atomic service with similar functionality. APBL which indicates the order of a collection of activities without the details of execution is to implement the additional function.Secondly, We focus on an ontology-based Context Model, called CUB-ONT, which derives from a set of descriptive contextual information for sharing common knowledge and logical inference, it includes CUB_SERVICE_ONT and CUB_USER_ONT.Thirdly, we propose an comprehensive approach for supporting automatic context-aware service discover and selection in a dynamic environment. In order to publish different kind of services and look up services automatically, we extended UDDI to add semantic-based service categorization and service request enhancement as separate layers on top of the UDDI. It is compliant with the universally adopted standard for service discovery. Service communities that are used to organize the large and heterogeneous service space, it also is a distributed and dynamic environment. Therefore, We propose then using multiobjective optimisation techniques to find a set of optimal solutions from which a user can choose the most interesting tradeoff in single service selection or compostion scenarios, namely, CUBSDSA and CUB_CWSDSM. They are both based on Context of mobile user and Qos Attributes of Service. For different type service, we propose a common Service Invocation Model.In order to verify the feasibility of our proposal, we have developed a preliminary prototype of system components and function that are considered to be critical to the viability of our proposal. At last we conducted serveral experiments in the prototype. Experimental evaluation results demonstrate that the CUB_SDSA algorithm provides a scalable solution to deal with the context-aware service selection problem in single service selection scenarios and CUB CWSDSM approach provides and a flexible solution in the context of composition scenarios. We will make a further step to develop highly automated and dynamically adaptive mobile commerce systems in the future.