A system for computer-aided guided interactive diagnosis is presented. It is based on dynamic taxonomies, a knowledge management model that allows the guided interactive exploration of complex information bases. Clinical diagnosis is performed by exploring and thinning out candidate pathologies on the basis of clinical signs and other observable features in a guided yet practitioner-centered way. Early experience with this approach is discussed in terms of opportunities for a global approach to diagnosis and treatment.

Abstract A new search paradigm, in which the primary user activity is the guided exploration of a complex information space rather than the retrieval of items based on precise specifications, is proposed. The author claims that this paradigm is the norm in most practical applications, and that solutions based on traditional search methods are not effective in this context. He then presents a solution based on dynamic taxonomies, a knowledge management model that effectively guides users to reach their goal while giving them total freedom in exploring the information base. Applications, benefits, and current research are discussed.

A guided interactive approach to computer-aided medical diagnosis is presented. This approach is based on dynamic taxonomies, a knowledge management model that allows the guided interactive exploration of complex information bases. The problem of the diagnosis of pathologies is recasted as a problem of exploring and thinning out candidate pathologies on the basis of symptoms and other observable features.

A novel taxonomic model for structuring and accessing large heterogeneous information bases is presented. The model is designed to simplify both classification and access by computer-illiterate people. It defines simple and intuitive operations to access large information bases at the conceptual level and at different levels of abstraction, in a totally assisted way, through a simple, yet effective visual interface. The model can also be used to summarize result sets computed by other query methods, such as information retrieval, shape retrieval, etc., and to provide user maps for complex hypermedia networks. The experience gained by applying this model to commercial applications is reported.

A “divide and conquer” strategy to compute natural joins by sequential scans on unordered relations is described. This strategy is shown to always he better than merging SCBIIS when both relations must he sorted before joining, and generally better in practical cases when only the largest relation mutt be sorted.

The hot-set model, characterizing the buffer requirements of relational queries, is presented. This model allows the system to determine the optimal buffer space to be allocated to a query; it can also be used by the query optimizer to derive efficient execution plans accounting for the available buffer space, and by a query scheduler to prevent thrashing. The hot-set model is compared with the working-set model. A simulation study is presented.

Heuristic strategies to solve multirelational queries in local area networks using broadcast routing are presented. In broadcast routing (used in broadcast and ring networks), data transmitted by one site are received by all the other network sites. This fact is exploited to derive new strategies to minimize a linear combination of query response and total time.