recently, some researchers have found that the abounding search engines cannot support exploratory search effectively. In such case, it requires the search engines know better about the imprecise queries provided by the end users. Actually, it's hard for the users to formulate the queries, not alone understand by the engines. However, in our study, we find that the search logs in the web community are significantly beneficial for the exploratory search, because various interests' communities exist in the large-scale web community. In this paper, we treat the search logs in the distributed search servers as footprints. And with these footprints, this study proposes an adaptive method to support effective searching over large-scale web documents. In the adaptive method, logistic regression with trust region applied in Map-reduce environment is devised, and it processes the large-scale web documents in parallel. Thus it can effectively classify the web documents queried by the dynamic imprecise searching. With these results, the method organizes them with frequency tree which shares redundant contents and records the counting for ranking. Extensive experiments demonstrate the merits of our adaptive method to support the exploratory search.
I. INTRODUCTIONWhile key word search is a well-known conventional implementation in current search engines, it's hard to respond to imprecise queries. Formulating queries for all this information is quite challenging, especially for a user who doesn't know well about this domain (e.g. a young academic student). Accordingly, many researchers have found that exploratory search happens very often, and it's essential to study how to help users to conduct effective exploratory search [1][2][3][4][5][6][7]. 1 It's hard to explore into the reasonable information space because results are mostly through static hyperlinks. Therefore, a main study point in this paper is how to provide user adaptive support of the information search, and an adaptive framework which explore the information space in the light of footprints [3].One of the biggest challenges for exploratory search might be the performance over large-scale and distributed web documents. Exploratory search is more complicated than the item-query techniques. Hence, this paper devises
Mobile IM (Instant Messaging) users are more frequently online and offline than fixed users. It incurs large presence message traffic that significantly consumes the limited service resources. Some studies indicate that presence delayed update can effectively reduce the traffic. Firstly, we analyze presence update process and research presence distribution. Secondly, we use mathematical model to analyze presence delayed update. At last, we modify existing simulation methods to validate the model in respect of accuracy and effectiveness.
This paper presents the idea of recaption cost to characterize WAP sources of images and improve the classic cache removal policy. Then It gets a new policy of image caches, LFU based on recaption cost; using the algorithm in the wap browser, It adopts the strategy of destroying cached images when exiting the browser, in this way, it not only guarantees obtaining newest images when starting browser next time, but also improves the overall performance of the browser.
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