Intelligent Support for Multidimensional Data Analysis in Environmental Epidemiology

Within the scope of the project CARLOS (Cancer Registry Lower{Saxony), a software system | CARESS (CARLOS Epidemiological and Statistical Data Exploration System) | was developed to support modeling and conducting of descriptive epidemiologic studies. The fundamental idea was to implement a powerful core of a system for statistical analysis, which is easily extensible with regard to both data types and algorithms for processing the data. We followed a knowledge-based approach, i. e. a strict separation of data and knowledge on the one hand and the control cycle processing this knowledge on the other. The main concepts concerning data structures, methods, and data processing are presented. Special emphasis is put on the underlying data analysis model and the user interface, namely a visual workbench providing easy access to the whole trail of a study and all relevant data and knowledge. CARESS aims at novel techniques for analysing cancer clustering using advanced database technology to support multidimensional analysis. Apart from establishing a population{based cancer registry in Lower{Saxony, a federal state of Germany, the project CARLOS also aims at providing software support for all steps of cancer registration 1]. Especially, novel techniques for analysing cancer clustering using an advanced analysis system and database technology are being developed and implemented. A database documents cancer cases in a predeened area. Incoming data is stored and epidemiologists may use it for describing the distribution of disease or testing hypotheses on cancer clusters and their determinants. Additional, e. g. spatial data is related to data about cancer cases to support epidemiologists in generating hypotheses about cancer clustering. Events in time and space are used to trigger rules evaluating time{space{conngurations constituting candidates for such clusters. To overcome cancer is not only still an immense medical task but also an increasing interdisciplinary task. The main aim of investigating causality is to improve prevention. To achieve this, computer supported population{based cancer registries form an important foundation. They improve methodical founded