Vol.6 No.3 September 1, 2007  
Logging Traces of Web Activity

Editorial (pp193-195)
        A. Edmonds, K. Hawkey, B.J. Jansen, M. Kellar, and D. Turnbull

Research Articles:
Integrating Interaction Design and Log an Analysis: Bridging the Gap with UML, XML, and XMI (pp196-221)
        G. Muresan
In this paper, we describe and discuss a formal methodology that integrates the conceptual design of the user interaction for interactive systems with the analysis of the interaction logs. It is based on (i) formalizing, via UML state diagrams, the functionality that is supported by a system and the valid interactions that can take place; (ii) deriving XML schemas for capturing the interactions in activity logs; (iii) deriving log parsers that reveal the system states and the state transitions that took place during the interaction; and (iv) analyzing the state activities and the state transitions in order to describe the user interaction or to test some research hypotheses. While this approach is rather general and can be applied in studying a variety of interactive systems, it has been devised and applied in research work on exploratory information retrieval, where the focus is on studying the interaction and on finding interaction patterns. The details of the methodology are discussed and exemplified for a mediated retrieval experiment.

Behavior-Based Web Page Evaluation (pp222-242)
        G. Velayathan and S.Yamada
This paper describes our efforts to investigate factors in user browsing behavior to automatically evaluate Web pages that the user shows interest in. To evaluate Web pages automatically, we developed a client-side logging/analyzing tool: the GINIS Framework. We do not focus on just clicking, scrolling, navigation, or duration of visit alone, but we propose integrating these patterns of interaction to recognize and evaluate user response to a given Web page. Unlike most previous Web studies analyzing access through proxies or servers, this work focuses primarily on client-side user behavior using a customized Web browser. First, GINIS unobtrusively gathers logs of user behavior through the user’s natural interaction with the Web browser. Then, it analyses the logs and extracts effective rules to evaluate Web pages using a C4.5 machine learning system. Eventually, GINIS becomes able to automatically evaluate Web pages using these learned rules, after which the evaluation can be utilized for a variety of user profiling applications. We successfully confirmed, for example, that time spent on a Web page is not the most important factor in predicting interest from behavior, which conflicts with the findings of most previous studies.

Instrumenting the Dynamic Web (pp243-260)
        A. Edmonds, R.W. White, D. Morris, and S. M. Drucker
One of the most critical driving forces in the evolution of interfaces on the Internet has been the logging built into common Web servers and the decade-long deployment of analytics based upon this data source.  Page-view logging has slowly moved to callback systems using client-side scripting to capture more aspects of the user experience.  With the rise of JavaScript-based client-side interactivity and, more recently, asynchronous Javascript and XML (AJAX), server-side logging is less able to capture the user experience of Web sites and applications that are rising in complexity.  We present a new technique for the in-page logging of interaction events that will help interaction designers make more informed design decisions based on how users are interacting with their systems.  The potential benefit of our technique is demonstrated in a case study with a working system.

An Integrated Technique for Web Site Usage Semantic Analysis: the Organ System  (pp261-280)
        J. Garofalakis, T. Giannakoudi, and E. Sakkopoulos
In this work, a new log analysis system is proposed and implemented, called ORGAN (Ontology-oRiented usaGe ANalysis system). ORGAN aims to enhance and ease log analysis by using semantic knowledge.It is able to offer typical statistical analysis of Web usage logs taking into consideration at the same time site’s underlying semantics. We evaluated ORGAN using Web site data for different cases to verify and exhibit its promising behavior. The experimental outcomes were encouraging and valuable conclusions for the Web site usage under analysis were reached. Consequently, we believe and show paradigms that ORGAN could become a useful tool for Web log analysts and assist the Web site managers in the decision-making for reorganization tasks. Finally, we discuss open problems to motivate further research efforts towards the incorporation of semantic Web technologies into Web site log mining analysis.

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