Search

With the development of Web technology and more kinds of information, how to provide high quality, relevant search results become a huge challenge to the current Web search engines. We analyze the shortcomings of PageRank algorithm and Weighted PageRank algorithms and make targeted improvements. By judging the relation between different web pages based on web content, the improved PageRank algorithm improved the user search precision.

View of web comment is affect the potential consumers purchasing decisions and the impact on the final results of the growing corporate sales significantly, thus the evolution of the view is an important study more and more. Based on complex network, we build dynamics model in web comment mode to analyze the basic characteristics of review evolution. Based on simulation analysis, we provide suggestions on effective web comment management for enterprises.

There is a large class of applications that high-frequency data produced continuously in 7 days per week and 24 hours per day. The traditional data processing system can not deal with them efficiently because of the active data pushing and the passive queries. With the purpose of dealing with these data, data stream management systems (DSMS) appeared. For the geographical distribution of data streams, distributed data stream processing systems are studied recently. As an important aspect of distributed data stream management system (DDSMS), load management can balance system load incurred by unpredictable incoming data stream and inappropriate query operators' distribution. Load-shedding acts as an important role in load management of distributed data stream system. In this paper, a new linear programming based load-shedding strategy is proposed to degrade system load on a simple query network with network constraint. Compared with previous work which concentrates on one factor only, it takes two factors, include CPU capacity and network constraints. Experiments indicate that it can increase system stability and to decrease the loss of query accuracy with low extra cost.

Ear and face based multimodal recognition could fully utilize their connection relationship, and implement recognizing people without their cooperation because of ear’s special physiological location and structure. In this paper akernel-based feature fusion algorithm is presented and applied to multimodal recognition based on fusing ear and face. With the algorithm, the associated feature vectors of ear and profile face are established for the following classification. The experimental results show that the method is efficient for feature fusion, and the multimodal recognition based on ear and profile face performs better than ear or profile face unimodal biometric recognition.