1,870 research outputs found

    Web-log mining for predictive web caching

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    "A Two-Stage Prediction Model for Web Page Transition"

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    Utilizing data from a log file, a two-stage model for step-ahead web page prediction that permits adaptive page customization in real-time is proposed. The first stage predicts the next page of a viewer based on a variant of a Markov transition matrix computed from page sequences of other visitors who read the same pages as that viewer did thus far. The second stage re-analyzes the incorrect exit/continuation predictions of the first stage through data mining, incorporating the visitor's viewing behavior observed from the log file. The two-stage process takes advantage of a robust, theory-driven nature of statistical modeling for extracting the overall feature of the data, and a flexible, data-driven nature of data mining to capture any idiosyncrasies and complications unresolved in the first stage. The empirical result with a test site implies that the first stage alone is sufficiently accurate (50.3%) in predicting page transitions. Prediction of site exit was even better with 100% of the exit and 90.8% of the continuation predictions being correct. The result was compared against other models for predictive accuracy.

    Web Proxy Cache Replacement Policies Using Decision Tree (DT) Machine Learning Technique for Enhanced Performance of Web Proxy

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    Web cache is a mechanism for the temporary storage (caching) of web documents, such as HTML pages and images, to reduce bandwidth usage, server load, and perceived lag. A web cache stores the copies of documents passing through it and any subsequent requests may be satisfied from the cache if certain conditions are met. In this paper, Decision Tree (DT ) a machine learning technique has been used to increase the performance of traditional Web proxy caching policies such as SIZE, and Hybrid. Decision Tree (DT) is used and integrated with traditional Web proxy caching techniques to form better caching approaches known as DT - SIZE and DT - Hybrid. The proposed approaches are evaluated by trace - driven simulation and compared with traditional Web proxy caching techniques. Experimental results have revealed that the proposed DT - SIZE and DT - Hybrid significantly increased Pure Hit - Ratio, Byte Hit - Ratio and reduced the latency when compared with SIZE and Hybrid

    Exploiting Data Mining Techniques for Broadcasting Data in Mobile Computing Environments

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    Cataloged from PDF version of article.Mobile computers can be equipped with wireless communication devices that enable users to access data services from any location. In wireless communication, the server-to-client (downlink) communication bandwidth is much higher than the client-to-server (uplink) communication bandwidth. This asymmetry makes the dissemination of data to client machines a desirable approach. However, dissemination of data by broadcasting may induce high access latency in case the number of broadcast data items is large. In this paper, we propose two methods aiming to reduce client access latency of broadcast data. Our methods are based on analyzing the broadcast history (i.e., the chronological sequence of items that have been requested by clients) using data mining techniques. With the first method, the data items in the broadcast disk are organized in such a way that the items requested subsequently are placed close to each other. The second method focuses on improving the cache hit ratio to be able to decrease the access latency. It enables clients to prefetch the data from the broadcast disk based on the rules extracted from previous data request patterns. The proposed methods are implemented on a Web log to estimate their effectiveness. It is shown through performance experiments that the proposed rule-based methods are effective in improving the system performance in terms of the average latency as well as the cache hit ratio of mobile clients

    REVIEW PAPER ON WEB PAGE PREDICTION USING DATA MINING

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    The continuous growth of the World Wide Web imposes the need of new methods of design and determines how to access a web page in the web usage mining by performing preprocessing of the data in a web page and development of on-line information services. The need for predicting the user’s needs in order to improve the usability and user retention of a web site is more than evident now a day. Without proper guidance, a visitor often wanders aimlessly without visiting important pages, loses interest, and leaves the site sooner than expected. In proposed system focus on investigating efficient and effective sequential access pattern mining techniques for web usage data. The mined patterns are then used for matching and generating web links for online recommendations. A web page of interest application will be developed for evaluating the quality and effectiveness of the discovered knowledge.   Keyword: Webpage Prediction, Web Mining, MRF, ANN, KNN, GA
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