Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Combining data mining and human expertise for making decisions, sense and policies

Combining data mining and human expertise for making decisions, sense and policies A major challenge facing management in developed countries is improving the performance of knowledge and service workers, i.e. the decision and policy makers. In a developing country such as South Africa, with a welldeveloped business sector, this need, especially in government, is even more crucial. South Africa has to face many new challenges in the 21st century growing environmental concerns, massive social and economic inequalities, high occurrences of HIV, low productivity, massive unemployment and the nations evolving role in Africa, amongst others. The importance of a sound science and technology policy framework to address these pressing issues cannot be overemphasised This paper discusses the construction of a knowledgebase from a data repository concerning a South African National Research and Technology NRT Audit. This knowledgebase is to be used as an aid when developing a science and technology policy framework for South Africa. The knowledgebase is constructed using the cooperative inductive learning team CILT approach, which combines diverse data mining tools and human expertise into a cooperative learning system. In this approach, each data mining tool constructs a model of the knowledge as contained in the data repository, thus providing an automated tool to make sense of the knowledge embedded therein. That is, the data mining tools learn from the data in order to obtain new insights. The system also incorporates human domain expertise through the computational modelling of the human subject knowledge. The knowledge, as obtained during team learning, is stored in a team knowledgebase. Results indicate that the CILT learning team approach can be successfully used to make sense of the vast amounts of data collected and provide a knowledge repository for further decision making and policy formulation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Systems and Information Technology Emerald Publishing

Combining data mining and human expertise for making decisions, sense and policies

Download PDF
24 pages

Loading next page...
 
/lp/emerald-publishing/combining-data-mining-and-human-expertise-for-making-decisions-sense-zrlzYWNaWm
Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1328-7265
DOI
10.1108/13287260080000754
Publisher site
See Article on Publisher Site

Abstract

A major challenge facing management in developed countries is improving the performance of knowledge and service workers, i.e. the decision and policy makers. In a developing country such as South Africa, with a welldeveloped business sector, this need, especially in government, is even more crucial. South Africa has to face many new challenges in the 21st century growing environmental concerns, massive social and economic inequalities, high occurrences of HIV, low productivity, massive unemployment and the nations evolving role in Africa, amongst others. The importance of a sound science and technology policy framework to address these pressing issues cannot be overemphasised This paper discusses the construction of a knowledgebase from a data repository concerning a South African National Research and Technology NRT Audit. This knowledgebase is to be used as an aid when developing a science and technology policy framework for South Africa. The knowledgebase is constructed using the cooperative inductive learning team CILT approach, which combines diverse data mining tools and human expertise into a cooperative learning system. In this approach, each data mining tool constructs a model of the knowledge as contained in the data repository, thus providing an automated tool to make sense of the knowledge embedded therein. That is, the data mining tools learn from the data in order to obtain new insights. The system also incorporates human domain expertise through the computational modelling of the human subject knowledge. The knowledge, as obtained during team learning, is stored in a team knowledgebase. Results indicate that the CILT learning team approach can be successfully used to make sense of the vast amounts of data collected and provide a knowledge repository for further decision making and policy formulation.

Journal

Journal of Systems and Information Technology – Emerald Publishing

Published: Dec 1, 2000

There are no references for this article.