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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)
Because of data privacy regulations, census data are available for analysis only in aggregated form. Primary data (responses of persons) are aggre-gated 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)
Ubiquitous Knowledge Discovery is a new research area at the intersection of machine learning and data mining with mobile and distributed systems. In this paper the main characteristics of the objects of study are defined and a high-level framework for analyzing ubiquitous knowledge discovery systems is introduced. Next, a number of examples from a broad(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)
Androgen receptor (AR) signaling pathways are important for the survival and proliferation of prostate cancer cells. Because AR activity is facilitated by distinct coregulatory factors and complexes, it is conceivable that some of these proteins might also play a role in promoting prostate oncogenesis. The multisubunit Mediator complex is an important(More)
Due to the increased computerization of many industrial, scientific, and public sectors, the growth of available digital data is proceeding at an unprecedented rate. As a result, the demand for highly automated data analysis and interpretation systems and tools is growing fast. Besides data growth, there has been an accelerating trend towards the sharing of(More)
Spatio-temporal data mining is an emerging research area dedicated to the development and application of novel computational techniques for the analysis of large spatio-temporal databases. The main impulse to research in this subfield of data mining comes from the large amount of & spatial data made available by GIS, CAD, robotics and computer vision(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)