We need more sophisticated methods for gathering, massaging and making connections among all the pieces of information that enter our lives each day -- everything from e-mail to Web pages to phone numbers and more. So when I see useful tools, I pay attention.

In our Web-connected world, a business enterprise must have timely information in order to survive. But the applications that collect and manage the information may have been developed independently, over years, using different products and technology.

How can such information -- scattered across multiple databases and applications -- be collected and integrated for access in real time?

Grokker builds precise and detailed knowledge maps containing visual cues and relationships between the data. The map itself contains powerful metadata that vividly describes the “nature” of the data collection. The Grokker product enables map generation and the ability to collaborate, extend, edit, delete, save, and share any attribute or subset of the map. This approach allows the Grokker to negotiate any type of network, file system, or database saving time, resources, and most importantly, generates far more useful results.

ACCESS: Get a big picture view of how large collections of information fit together in a contextual setting.

ORGANIZE: All data sources can be consolidated into a visual information framework.

At a time when the valley's digerati are bemoaning a technology industry recession and the death of innovation, Mr. Hawken's Grokker software, which is intended to allow personal-computer users to visually make sense of collections of thousands or hundreds of thousands of text documents, is creating a buzz. The software is attracting significant interest from large corporations and universities.

How to Design Programs: An Introduction to Programming and Computing

The handling of user preferences is becoming an increasingly important issue in present-day information systems. Among others, preferences are used for information filtering and extraction to reduce the volume of data presented to the user. They are also used to keep track of user profiles and formulate policies to improve and automate decision making.

WebTheme combines the power of software agent-based information retrieval with visual analytics to provide users with a new tool for understanding web information.

WebTheme allows users to both quickly comprehend large collections of information from the Web and drill down into interesting portions of a collection. Software agents work for users to perform controlled harvesting of web material of interest.

Visualization and analysis tools allow exploration of the resulting document space. Information spaces are organized and presented according to their topical context. Tools that display how documents were collected by the agents, where they were gathered, and how they are linked further enhance users' understanding of information and its context.

This paper describes an approach to derive assessments about information sources based on individual feedback about the sources.

We describe TRELLIS, a system that helps users annotate their analysis of alternative information sources that can be contradictory and incomplete.

As the user makes a decision on which sources to dismiss and which to believe in making a final decision, TRELLIS captures the derivation of the decision in a semantic markup. TRELLIS then uses these annotations to derive an assessment of the source based on the annotations of many individuals.

In recent years, an increasing interest in recommendation systems has emerged both from the research and the application point of view and in both academic and commercial domains. The majority of comparison techniques used for formulating recommendations are based on set-operations over user-supplied terms or internal product computations on vectors encoding user preferences.

This paper proposes a recommendation algorithm based on user profiles and their dynamic adjustment according to user behavior, as well as dynamic management of communities, which contain "similar" and "relevant" users and which are created according to a classification algorithm. The algorithm is implemented on top of a community management mechanism.