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Web Usage Mining: Dynamic Methodology to Preprocessing Web Logs

Internet is a huge source of massive information for retrieving information and searching knowledge from WWW, leading to increase network traffic, access delay & server overload, which results in poor web services. With the use of Web-caching & web prefetching techniques to enhance the performance of web services where web mining techniques play an important role to decide which web object should be pre-fetched from server and stored in proxy cache memory so that the web object with high probability of request, in the next couple of days, serves as the base of the proxy cache. But for efficient web mining and to extract meaningful usage access pattern, the raw log file must be transformed into a meaningful & formatted file. This paper proposed a new dynamic preprocess technique to create a dynamic training dataset for prediction model using web mining, and Graph based substructure Pattern Mining (GSPAN) for improved preprocessing using proxy log. The proposed model would help in minimizing the cache size by 40% thus improving the overall performance.