WebFrame: In Pursuit of Computationally and Cognitively Efficient Web Mining

摘要

The goal of web mining is relatively simple: provide both computationally and cognitively efficient methods for improving the value of information to users of the WWW. The need for computational efficiency is well-recognized by the data mining community, which sprung from the database community concern for efficient manipulation of large datasets. The motivation for cognitive efficiency is more elusive but at least as important. In as much as cognitive efficiency can be informally construed as ease of understanding, then what is important is any tool or technique that presents cognitively manageable abstractions of large datasets. We present our initial development of a framework for gathering, analyzing, and redeploying web data. Not dissimilar to conventional data mining, the general idea is that good use of web data first requires the careful selection of data (both usage and content data), the deployment of appropriate learning methods, and the evaluation of the results of applying the results of learning in a web application. Our framework includes tools for building, using, and visualizing web abstractions. We present an example of the deployment of our framework to navigation improvement. The abstractions we develop are called Navigation Compression Models (NCMs), and we show a method for creating them, using them, and visualizing them to aid in their understanding.