Knowledge discovery in databases is a complex process concerned with the discovery of relationships and other descriptions from data. Knowledge discovery in spatial databases represents a particular case of discovery, allowing the discovery of relationships that exist between spatial and non-spatial data, and other data characteristics that aren’t explicitly stored in spatial databases. This pa...

Geographic data are characterized by huge volumes, lack of standards, multiplicity of data sources, multiscale requirements, and variability in time. These characteristics make geographic information complex and uncertain. At the same time, the important growth of the quantity of data manipulated and the necessity to make rapid decisions imposed the appearance and the great progress of new tool...

This chapter introduces the field of computational scientific discovery and provides a brief overview thereof. We first try to be more specific about what scientific discovery is and also place it in the broader context of the scientific enterprise. We discuss the components of scientific behavior, that is, the knowledge structures that arise in science and the processes that manipulate them. W...

Submitted to Visual Information Processing Workshop 2001 Abstract This paper describes the concept, and previous realisations, of multi-perspective images in nature, art and visualisation. By showing how distortions have been used for visualisation, it motivates the use of multiperspective images, which are similar in effect to object based distortions. A new API being developed to facilitate m...

Most video applications fail to capture in an efficient knowledge representation model interactions between subjects themselves and interactions between subjects and contextual objects of the observed scene. In this paper we propose a knowledge modelling format which allows efficient knowledge representation. Furthermore, we show how advanced algorithms of knowledge discovery can be applied fol...

This paper introduces the idea of using nonmonotonic inheritance networks for the storage and maintenance of knowledge discovered in data (revisable knowledge discovery in databases | RKDD). While existing data mining strategies for knowledge discovery in databases (KDD) typically involve initial structuring through the use of identiication trees and the subsequent extraction of rules from thes...

The essence of data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Existing data mining tools consider snapshots of data and therefore unable to handle the complexity of a dynamic environment, such as financial applications which contain a huge amount of data that changes over time. The knowledge discovered has limited value si...

People can acquire knowledge not only from media such as books, but also through interactions with other people. Automated dialog agents, however, typically limit their learning from labeled data, and programming. This thesis describes an approach that enables such agents to actively acquire new knowledge through spoken dialog interaction. To acquire knowledge in different situations, we propos...

A new framework for analyzing sequential or temporal data such as time series is proposed. It differs from other approaches by the special emphasis on the interpretability of the results, since interpretability is of vital importance for knowledge discovery, that is, the development of new knowledge (in the head of a human) from a list of discovered patterns. While traditional approaches try to...

We present an approach to exible querying by exploiting similarity knowledge hidden in the information base. The knowledge represents associations between the terms used in descriptions of objects. Central to our approach is a method for mining the database for similarity knowledge, representing this knowledge in a fuzzy relation, and utilizing it in softening of the query. The approach has bee...