Scott W. Cunningham

A process for mining science & technology documents databases, illustrated for the case of "knowledge discovery and data mining"

This paper presents a process of mining research & development abstract databases to profile current status and to project potential developments for target technologies, The process is called “technology opportunities analysis.” This article steps through the process using a sample data set of abstracts from the INSPEC database on the topic o “knowledge discovery and data mining.” The paper offers a set of specific indicators suitable for mining such databases to understand innovation prospects. In illustrating the uses of such indicators, it offers some insights into the status of knowledge discovery research.

Related problems of knowledge discovery

Research within the communities of informatics, scientometrics, cybermetrics and knowledge discovery has, until recently, occurred in comparative isolation. In spite of this isolation, there are common concerns and techniques that would benefit from a cross-fertilization of ideas. In this paper, these research areas are defined in terms of data, application and discipline. A shared set of analysis tools and theoretical approaches are identified and presented toward the goal of establishing a shared theoretical basis for the disciplinary problems.

Expert systems: Present and future

Abstract This paper aims to forecast the evolution of expert systems over the coming 5–10 years. To do so, the authors introduce a conceptual model that identifies four major areas that contribute to expert systems. The paper discusses advances in each of these four areas and relates these to changes in expert systems technology. The paper then ties quantitative trends with these qualitative advances to anticipate what expert systems will be like in the 1990s.