Moving Usability Testing onto the Web

Abstract: In order to remotely obtain detailed usability data by tracking user behaviors within a given web site, a server-based usability testing environment has been created. Web pages are annotated in such a way that arbitrary user actions (such as "mouse over link" or "click back button") can be selected for logging. In addition, the system allows the experiment designer to interleave interactive questions into the usability evaluation, which for instance could be triggered by a particular sequence of actions. The system works in conjunction with clustering and visualization algorithms that can be applied to the resulting log file data. A first version of the system has been used successfully to carry out a web usability evaluation

Revealing User Behaviour on the World-Wide Web

This paper presents the results of a qualitative study of user behaviour on the World-Wide Web. Eight participants were filmed whilst performing user-defined tasks and then asked to review the video-taped session during prompted recall. This data forms the basis for a series of descriptions of user behaviour and the postulation of a number of underlying cognitive mechanisms. Our results indicate that people: lack ready made search strategies, prefer alternatives that are visible, immediately available and familiar, choose the path of least resistance, exhibit social forms of behaviour, engage in parallel activities, object to misleadingly presented information, have trouble orienting, are late in using appropriate strategies, are sensitive to matters of time, and are emotionally involved in the activity. The paper ends with a discussion of how these results can contribute to our understanding of hypermedia

Collaborative Categorization on the Web

Collaborative categorization is an emerging direction for research and innovative applications. Arguably, collaborative categorization on the Web is an especially promising emerging form of collaborative Web systems because of both, the widespread use of the conventional Web and the emergence of the Semantic Web providing with more semantic information on Web data. This paper discusses this issue and proposes two approaches: collaborative categorization via category merging and collaborative categorization proper. The main advantage of the first approach is that it can be rather easily realized and implemented using existing systems such as Web browsers and mail clients. A prototype system for collaborative Web usage that uses category merging for collaborative categorization is described and the results of field experiments using it are reported. The second approach, called collaborative categorization proper, however, is more general and scales better. The data structure and user interface aspects of an approach to collaborative categorization proper are discussed

Integrating E-Commerce and Data Mining: Architecture and Challenges

We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our expe-rience at Blue Martini Software, for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e.g., clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We con-clude with a set of challenges.Comment: KDD workshop: WebKDD 200

Log Pre-Processing and Grammatical Inference for Web Usage Mining

International audienceIn this paper, we propose a Web Usage Mining pre-processing method to retrieve missing data from the server log files. Moreover, we propose two levels of evaluation: directly on reconstructed data, but also after a machine learning step by evaluating inferred grammatical models. We conducted some experiments and we showed that our algorithm improves the quality of user data

Adaptive Replicated Web Documents.

Caching and replication techniques can improve latency of the Web, while reducing network traffic and balancing load among servers. However, no single strategy is optimal for replicating all documents. Depending on its access pattern, each document should use the policy that suits it best. This paper presents an architecture for adaptive replicated documents. Each adaptive document monitors its access pattern, and uses it to determine which strategy it should follow. When a change is detected in its access pattern, it re-evaluates its strategy to adapt to the new conditions. Adaptation comes at an acceptable cost considering to the benefits of per-document replication strategies. vrije Universiteit Faculty of Mathematics and Computer Science 1 Introduction Most Web users suffer from slow document transfers. The reasons for such high latencies include distance between the user and the document, and load of the intermediate network. One common solution is to maintain copies of ..

Association Rules for Web Data Mining in WHOWEDA

The authors discuss association rules which can be discovered from Web data. The association rules are discussed within the scope of our WHOWEDA (warehouse of Web data) project. WHOWEDA is supported by a Web data model and a set of algebraic operators. The Web data model allows a uniform and integrated view of Web data gathered using a user\u27\u27s query graph. A user\u27\u27s query graph describes the query by example (what the user perceives as the query) and the Web coupling query gathers instances of such a query graph from the Web and stores them in the form of subgraphs (called Web tuples) in a Web table. We discuss association rules within this domain. An association rule defines an association between the nodes and links attributes of Web tuples within a Web table. There are two different classes of association rules that can be developed from data in a Web table. There are two different classes of association rules that can be developed from data in a Web table. Node-to-node associations are those rules that relate the content (defined by metadata attributes) between two or more nodes within a Web tuple. Link associations are rules that show the connectivity of different URLs. Distinguishing the two types of associations provides a view of the structure of the Web data. The goal of performing Web association mining on Web data is to better organize searching patterns through hyperlinked document

点击流数据仓库的构建与多维分析

介绍点击流数据仓库的多维建模技术,在此基础上以“平和网“的日志数据为例,利用SQl SErVEr2008构建点击流数据仓库,并对其进行多维分析研究