TopCat (topic categories) is a technique for identifying topics that recur in articles in a text corpus. Natural language processing techniques are used to identify key entities in individual articles, allowing us to represent an article as a set of items. This allows us to view the problem in a database/data mining context: Identifying related groups of items. We present a novel method for identifying related items based on traditional data mining techniques. Frequent itemsets are generated from the groups of items, followed by clusters formed with a hypergraph partitioning scheme. We present an evaluation against a manually categorized ground truth news corpus; it shows this technique is effective in identifying topics in collections of news articles.

It is estimated that over seven billion static pages exist in the Web today, and backend databases can potentially produce at least three times as many dynamic pages. However, the best search engines index only approximately 20% of the static pages. So the real question is: While the Web is certainly the most amazing and comprehensive information source ever created, are you really getting all the information you need for your specific purpose? The answer to this question is mostly “yes” for the individual user, who uses the Web as an information source for casual purposes. However, for an individual who uses the Web as an essential and comprehensive source of information—for business or research—the answer is quite the opposite. Even a sophisticated Web user requires a significant amount of time and effort to find all of the information needed for a given task. In this paper the concept of Web Business Intelligence (WBI) is introduced, an emerging class of software that leverages the unprecedented content on the Web to extract actionable knowledge in an organizational setting. The contributions include an architecture for WBI, a survey of technologies relevant to the various components of the architecture, and illustration of the value of WBI by means of a detailed example from the e-finance domain. This article concludes with a discussion on the future of WBI.

The discipline of Web Usage Mining has grown rapidly in the past few years, despite the crash of the e-commerce boom of the late 1990s. Web Usage Mining is the application of data mining techniques to Web clickstream data in order to extract usage patterns. Yet, with all of the resources put into the problem, claims of success have been limited and are often tied to specific Web site properties that are not found in general. One reason for the limited success has been a component of Web Usage Mining that is often overlooked---the need to understand the content and structure of a Web site. The processing and quantification of a Web sites content and structure for all but completely static and single frame Web sites is arguably one of the most difficult tasks to automate in the Web Usage Mining process. This article shows that, not only is the Web Usage Mining process enhanced by content and structure, it cannot be completed without it. The results of experiments run on data from a large e-commerce site are presented to show that proper preprocessing cannot be completed without the use of Web site content and structure, and that the effectiveness of pattern analysis is greatly enhanced.

Creating innovative interactive multimedia presentations requires a great deal of time, skill, and effort. We have developed a multimedia synchronization toolkit, called Nsync (pronounced 'in-sync'), to address the complicated issues inherent in designing flexible, interactive multimedia presentations. The toolkit consists of two major components, a declarative synchronization definition language and a run-time presentation management system. The synchronization definition language supports the specification of synchronous interaction, asynchronous interaction, and fine-grained relationships among multiple media objects. The language also supports the combination of interactive behavior and fine-grained relationships through the use of conjunctive and disjunctive operators. We illustrate the use of the language through several examples. We argue that with the inclusion of asynchronous interaction, pre-computed playout schedules are too inflexible, thus we have developed a new presentat...

Application of data mining techniques to the World Wide Web, referred to as Web mining, has been the focus of several recent research projects and papers. However, there is no established vocabulary, leading to confusion when comparing research efforts. The term Web mining has been used in two distinct ways. The first, called Web content mining in this paper, is the process of information discovery from sources across the World Wide Web. The second, called Web usage mining, is the process of mining for user browsing and access patterns. We define Web mining and present an overview of the various research issues, techniques, and development efforts. We briefly describe WEBMINER, a system for Web usage mining, and conclude the paper by listing research issues.