In this paper we want to discuss the usage of Web 2.0 techniques to realize information visualization based exploration and annotation of huge volume, semi-structured data, and in particular high throughput bioimage series. To this end, we developed a toolbox for a graphical representation of different displays in a browser context which can be used for image database exploration in a link & brush fashion. The Web based approach proved to be capable of information visualization tasks and supports collaboration of several users at arbitrary locations.

The ability to assess the relevance of topics and related sources in information-rich environments is a key to success when scanning business environments. This paper introduces a hybrid system to support managerial information gathering. The system is made up of three components: 1) a hierarchical hyperbolic SOM for structuring the information environment and visualizing the intensity of news activity with respect to identified topics, 2) a spreading activation network for the selection of the most relevant information sources with respect to an already existing knowledge infrastructure, and 3) measures of interestingness for association rules as well as statistical testing facilitates the monitoring of already identified topics. Embedding the system by a framework describing three modes of human information seeking behavior endorses an active organization, exploration and selection of information that matches the needs of decision makers in all stages of the information gathering process. By applying our system in the domain of the hotel industry we demonstrate how typical information gathering tasks are supported. Moreover, we present an empirical study investigating the effectiveness and efficiency of the visualization framework of our system.

MOTIVATION: Modern high-throughput sequencing technologies enable the simultaneous analysis of organisms in an environment. The analysis of species diversity and the binning of DNA fragments of non-sequenced species for assembly are two major challenges in sequence analysis. To achieve reasonable binnings and classifications, DNA fragment structure has to be represented appropriately, so it can be processed by machine learning algorithms. RESULTS: Hierarchically growing hyperbolic Self-Organizing maps (H(2)SOMs) are trained to cluster small variable-length DNA fragments (0.2-50 kb) of 350 prokaryotic organisms at six taxonomic ranks Superkingdom, Phylum, Class, Order, Genus and Species in the Tree of Life. DNA fragments are mapped to three different types of feature vectors based on the genomic signature: basic features, features considering the importance of oligonucleotide patterns as well as contrast enhanced features. The H (2)SOM classifier achieves high classification rates while at the same time its visualization allows further insights into the projected data and has the potential to support binning of short sequence reads, because DNA fragments can be grouped into phylogenetic groups. AVAILABILITY: An implementation of the H(2)HSOM classifier in Matlab is provided at www.techfak.uni-bielefeld.de/ags/ani/projects/HHSOMSeqData.

We propose the combination of two recently introduced methods for the interactive visual data mining of large collections of data. Both hyperbolic multidimensional scaling (HMDS) and hyperbolic self-organizing maps (HSOM) employ the extraordinary advantages of the hyperbolic plane (H2): (i) the underlying space grows exponentially with its radius around each point deal for embedding high-dimensional (or hierarchical) data; (ii) the Poincare model of the IH/sup 2/ exhibits a fish-eye perspective with a focus area and a context preserving surrounding; (in) the mouse binding of focus-transfer allows intuitive interactive navigation. The HMDS approach extends multidimensional scaling and generates a spatial embedding of the data representing their dissimilarity structure as faithfully as possible. It is very suitable for interactive browsing of data object collections, but calls for batch precomputation for larger collection sizes. The HSOM is an extension of Kohonen's self-organizing map and generates a partitioning of the data collection assigned to an IH/sup 2/ tessellating grid. While the algorithm's complexity is linear in the collection size, the data browsing is rigidly bound to the underlying grid. By integrating the two approaches, we gain the synergetic effect of adding advantages of both. And the hybrid architecture uses consistently the IH/sup 2/ visualization and navigation concept. We present the successfully application to a text mining example involving the Reuters-21578 text corpus.

We introduce a new type of Self-Organizing Map (SOM) to navigate in the Semantic Space of large text collections. We propose a “hyper- bolic SOM” (HSOM) based on a regular tesselation of the hyperbolic plane, which is a non-euclidean space characterized by constant negative gaussian curvature. The exponentially increasing size of a neighborhood around a point in hyperbolic space provides more freedom to map the complex information space arising from language into spatial relations. We describe experiments, showing that the HSOM can successfully be applied to text categorization tasks and yields results comparable to other state-of-the-art methods.