Thomas Anthony Cofino

Measuring success

COMMUNICATIONS OF THE ACM August 2000/Vol. 43, No. 8 53 The structure of the Web is rapidly evolving from a loose collection of Web sites into organized marketplaces. The phenomena of aggregation, portals, large enterprise sites, and business-to-business applications are resulting in centralized, virtual places, through which millions of visitors pass. With this development, it becomes possible to gather unprecedented amounts of data about individuals. Data sources capturing purchase histories, casual browsing habits, financial activities, credit histories, and demographics can be combined to construct highly detailed personal profiles. Not only is it possible to collect vast amounts of data, it is vital for e-businesses to be able exploit the data effectively. In the Internet environment, products and services are constantly in danger of becoming commodities, shoppers can explore competing Web sites without leaving their chairs, and bots and agents MEASURING SUCCESS Edith Schonberg, Thomas Cofino, Robert Hoch, Mark Podlaseck, and Susan L. Spraragen

Glossary extraction and utilization in the information search and delivery system for IBM technical support

In this paper we describe the practical aspects of extracting and using a glossary for a selected technical domain. We first describe the existing glossary extraction process, as applied to general corpora, and examine its shortcomings in the technical support domain. Then we propose a number of enhancements to it, including focusing the glossary on a selected domain context, providing support for multidomain glossaries, and importing domain-specific dictionaries. We apply our focused-glossary approach to the IBM Technical Support corpus and incorporate resulting glossaries within the information search and delivery system used by IBM Technical Support. We demonstrate the effectiveness of our approach by evaluating the quality of keywords and terms extracted from sample documents with the help of these glossaries.

Towards knowledge management in autonomic systems

The classical definition of knowledge management promises to get the right knowledge to the right people at the right time so they can make the best decision [G. Petrash, 1996]. Autonomic systems, on the other hand, are expected to find and apply the right knowledge for self-managing purposes without human intervention. This article discusses the components to be built around a system to enable self-healing and managing capabilities. These are defined and described in this article as self-knowledge, self-monitoring, self-learning, problem detection, diagnosis, and search and solution components. Interaction of these system components to make knowledge available for self-healing purposes is also discussed.