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Abstract. We study the impact of electronic markets on small, boutique firms selling presence goods or services--goods or services that must be consumed at the selling firm's location. These firms [...]

We use panel data from multiple wards from two hospitals spanning a three-year period to investigate the impact of automation of the core error prevention functions in hospitals on medical [...]

A study uses genetic programming to examine new ways for effective search and retrieval of information on the web.

The Internet and corporate Intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. In this paper, we argue that the ranking strategy should be context specific and we propose a new systematic method that can automatically generate ranking strategies for different contexts based on Genetic Programming (GP). The new method was tested on TREC data and the results are very promising. Index Terms--Intelligent information retrieval, personalization, search engine, term weighting, ranking function, text mining, genetic programming, contextual information retrieval, information routing, information retrieval.