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Context Recognition by User Situation Data Analysis (CONTEXT)

The Context project studies characterization and analysis of information about user context and its use in proactive adaptivity. In mobile and ubiquitous applications and systems, reacting to user context is a key component of proactivity: changes in the user’s situation are rapid and they are strongly reflected in the user’s needs and preferences.

Spatial and Temporal Data Mining

Study of place names, dialects, biodiversity, and climate, for example, results in data sets that have strong spatial and (possibly) temporal components. The research project looks at data mining methods that can be used to find spatial and temporal relationships in high-dimensional data. The project works in very close collaboration with the "Algorithmic and probabilistic methods in data mining" project.

Algorithmic and Probabilistic Methods in Data Mining

The project develops methods for the exploratory data analysis of large and highdimensional data sets. One of the themes has been finding frequent patterns in large collections of data. The pattern classes include ordered and unordered patterns. Currently areas of interest include condensed representations and the combination of combinatorial and probabilistic techniques for approximating distributions. For sequential data, interests are in algorithms for sequence segmentation under various restrictions and in discovery of order from unordered data sets.

CompGenome: New Computational Methods for Analyzing the Structural and Functional Landscapes of Mammalian Genomes

The availability of a large mass of genomic data will make it possible to study in detail the genomic landscape in humans and other mammalian organisms and to investigate the variation both within and between species. The CompGenome project will develop and apply computational tools for describing the genomic and functional variation between individuals and between species, and study the significance of these variations for the functions of genes.

Biomine: Knowledge discovery in biological databases

Public biological databases contain huge amounts of rich data, such as annotated sequences, proteins, domains, and orthology groups, genes and gene expressions, gene and protein interactions, scientific articles, and ontologies. The Biomine project develops methods for the analysis of such collections of data.

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