Researches On Service Context Processing Mechanism And Prediction Theory And Key Technology

The intelligent, personalized service is the development trend of next generation networks. The ability of across a variety of bearer networks and multiple operator domains, universal query access, integration by requirements, context information processing, mobile seamless application will be required and thus develop a user-centric humanization, intelligent service environment. The service context information processing mechanism is prerequisite to provide users with intelligent, personalized services, involving a variety of context information processing, including the homologous or heterogeneous context information processing of network, terminals and user environment. This paper mainly study on the research of service applications level of next generation network, including the future service context information processing mechanism, prediction theory and key technology.(1) First, for the service context information processing mechanism is not perfect currently, lack of the general and scalable support platform of context information processing. To establish the service context information processing architecture of universal significance, scalable and layered heterogeneous or homogeneous multi-service context information. To establish the scope and level framework of the relevant service context information to support the intelligence service information processing mechanism, and thus to provide users with personalized services. Based on the research of current context-aware technology to establish the entity architecture and interactive service provision model of comprehensive and systematic information.(2) Secondly, on that basis of the establishment of wholesome service context information processing architecture, further research on various prediction theory and key technologies based on future context.Currently,the context prediction mainly aims at the specific areas for example location, it does not establish the general prediction model and the prediction accuracy is not high, in order to solve the above problem, this paper proposes the approach of context prediction based on trust network and collaborative filtering algorithms, which combines users'trust value into the users'similarity, and establishes the universal general three-dimensional collaborative filtering model of user-item-context, combines with the users'context information to conduct prediction and reasoning. To solve the problems of proactive service based on context prediction technology, to lay the foundation for user service of guiding type or recommendation type personalized.(3) Again, whether is the context-aware systems based on current and historical context, or the prediction based on future context, the context data missing is an inevitable problem. This paper only aims at the specific areas of sensor-aware, to analyze the flow data form of context information, to make full use of data relevance between every collecting sensor and take spatial-temporal relationship into account, and then proposes an imputation technique for context data missing based on Spatial-Temporal and Association Rule Mining (STARM), to discuss comprehensively the data imputation approach of missing data, to improve the accuracy of imputation of missing data. Finally, the simulation experiment verifies the rationality and efficiency of STARM through temperature sensor data acquisition.(4) Finally, based on the proposed model framework, related theories, methods and algorithms, this paper designs and implements the service context processing platform, to achieve preliminary the test bed of service context processing with an integrated imitation, simulation and experiment, through the acquisition of analog sensor data and carry out fusion, reasoning and prediction of context information, to demonstrate the service context sample scenarios with proactive, intelligent service.