Advanced Software Development Workstation Project, phase 3

ACCESS provides a generic capability to develop software information system applications which are explicitly intended to facilitate software reuse. In addition, it provides the capability to retrofit existing large applications with a user friendly front end for preparation of input streams in a way that will reduce required training time, improve the productivity even of experienced users, and increase accuracy. Current and past work shows that ACCESS will be scalable to much larger object bases

An investigation into the application of machine learning in information retrieval

There is an increasing variety of online databases available which are also evergrowing in size. In retrieving information from these sources, it is important not only to have effective and efficient retrieval techniques but also to enable some form of adaptation to users’ specific needs. Frequent users, in particular, should be able to benefit from their high use of the information retrieval system. A machine learning approach can be applied to help the system adapt to users’ specific needs. It is argued that users have a particular context within which their queries are formed. It is likely that consecutive queries for a particular user will be related in that they will be part of the same context. Thus, a context learner is proposed. In this investigation, the context learner is used for enhancing document ordering in partial match systems