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The ARB (from Latin arbor, tree) project was initiated almost 10 years ago. The ARB program package comprises a variety of directly interacting software tools for sequence database maintenance and analysis which are controlled by a common graphical user interface. Although it was initially designed for ribosomal RNA data, it can be used for any nucleic and(More)
SubgroupMiner is an advanced subgroup mining system supporting multirelational hypotheses, efficient data base integration, discovery of causal subgroup structures, and visualization based interaction options. When searching for dependencies between subgroups and a target group, spatial subgroups with multirelational descriptions are explored. Search(More)
Because of data privacy regulations, census data are available for analysis only in aggregated form. Primary data (responses of persons) are aggregated in many cross tabulations for small geographical units. Thus the target objects of secondary analysis are small areas (enumeration districts or wards ). Any cell or marginal of a cross tabulation can be used(More)
As modern data mining applications increase in complexity, so too do their demands for resources. Grid computing is one of several emerging networked computing paradigms promising to meet the requirements of heterogeneous, large-scale, and distributed data mining applications. Despite this promise, there are still too many issues to be resolved before grid(More)
The organization and planning of services (e.g. shopping facilities, infrastructure) requires quantitative information about the number of customers and their frequency of visiting. In this paper we present a framework which enables the collection of quantitative visit information for arbitrary sets of locations in a distributed and privacy-preserving way.(More)
After the introduction and development of the relational database model between 1970 and the 1980s, this model proved to be insufficiently expressive for specific applications dealing with, for instance, temporal data, spatial data and multi-media data. From the mid-1980s, this has led to the development of domain-specific database systems, the first being(More)
In this paper, we discuss an application of spatial data mining to predict pedestrian flow in extensive road networks using a large biased sample. Existing out-of-the-box techniques are not able to appropriately deal with its challenges and constraints, in particular with sample selection bias. For this purpose, we introduce <i>s-knn-apriori</i>, an(More)