Large-scale session log analysis typically includes statistical methods and detailed log examinations. While both methods have merits, statistical methods can miss previously unknown subpopulations in the data and detailed analyses may have selection biases. We therefore built Session Viewer, a visualization tool to facilitate and bridge between statistical and detailed analyses. Taking a multiple-coordinated view approach, Session Viewer shows multiple session populations at the Aggregate, Multiple, and Detail data levels to support different analysis styles. To bridge between the statistical and the detailed analysis levels, Session Viewer provides fluid traversal between data levels and side-by-side comparison at all data levels. We describe an analysis of a large-scale web usage study to demonstrate the use of Session Viewer, where we quantified the importance of grouping sessions based on task type.

Clickstream data from e-commerce customers is a principal resource for evaluation of a website design. Many tools have been proposed for the visualization of Clickstream data [1, 2]. However, they do neither point to website design weaknesses nor translate into the improvements. Usually, an expert needs to go through an often lengthy and

Web usage mining, the analysis of user navigation paths through web sites, is a common technique for evaluating site designs or adaptive hypermedia techniques. However, often it is hard to relate aggregated clusters or measures to actual user navigation behavior. By contrast, basic graph-based visualizations of user navigation paths are easier to interpret, but it is difficult to find effective views that convey all the required information. In this paper we present the Navigation Visualizer, a web usage analysis tool that combines the two approaches. The Navigation Visualizer makes use of the rich data set that is collected by the Scone proxy-based web enhancement framework and facilitates dynamic selection of the data and interactive exploration with various layout mechanisms, color codings and markers. Several aggregated measures can be calculated and exported to statistical and data mining packages.

Discovering web navigational trends and understanding data mining results is undeniably advantageous to web designers and web-based application builders. It is also desirable to interactively investigate web access data and patterns, to allows ad-hoc discovery and examination of patterns that are not apriori known. Visualizing the usage data in the context of the web site structure is of major importance, as it puts web access requests and their connectivity in perspective. Various visualization tools have been developed for this task, but often fail to provide visual data mining functionalities to generate new patterns. Here we present our visual data mining system, WebViz, which allows interactive investigation of web usage data within their structure context, as well as ad-hoc knowledge pattern discovery on web navigational behaviour. Keywords—Visual Data Mining, Web Visualization 1

Problems related to navigation are known as the most frequently reported by users when browsing large Web sites. Users become quickly lost in large collections of pages especially if little contextual information is given to explore the information space. In this paper, we discuss the importance of providing users with a unified representation of structure and navigation information of Web sites as a way to prevent the lost-in-space phenomena. We present a notation to support navigation design of Web applications, the StateWebCharts, which explicitly represent structure and navigation in a seamless way. The Bifocal Tree, a technique originally developed for visualizing hierarchical structures, is extended and used to display StateWebCharts specifications, taking advantage of their characteristics to represent both structure and navigation information.

Web site structures are complex to analyze. Cross-referencing the web structure with navigational behaviour adds to the complexity of the analysis. However, this convoluted analysis is necessary to discover useful patterns and understand the navigational behaviour of web site visitors, whether to improve web site structures, provide intelligent on-line tools or o#er support to human decision makers. Moreover, interactive investigation of web access logs is often desired since it allows ad hoc discovery and examination of patterns not a priori known. Various visualization tools have been provided for this task but they often lack the functionality to conveniently generate new patterns. In this paper we propose a visualization tool to visualize web graphs, representations of web structure overlaid with information and pattern tiers. We also propose a web graph algebra to manipulate and combine web graphs and their layers in order to discover new patterns in an ad hoc manner.

In this paper, we present the first version of the MetroWeb tool designed to manage usability knowledge and to exploit this knowledge throughout evaluation process phases. The tool supports usability knowledge manipulation in general and specifically supports heuristic inspection. The usability evaluation process supported by the tool is decomposed into 4 main phases: planning, assessment, finalisation and follow-up.

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