Visualization interfaces that offer multiple coordinated views on a particular set of data items are useful for navigating and exploring complex information spaces. In this paper we address the problem of mining text information which is associated with structured data from relational data sources. We present a multi-view paradigm that closely integrates the analysis of unstructured text data with related structured data sets. Our concept brings together views on text similarity, text categories, and associated relational attributes for application fields like customer relationship management or business intelligence. A prototype is presented that exemplarily implements our multi-view framework.

A large amount of strategically relevant business information is contained in unstructured texts. While information brokering approaches are used to contextualize such documents and to generate metadata, text mining is used to explore large document spaces. So far, little attention has been paid on a value-adding combination of these technologies. We show how metadata and documents can be complementarily represented and used interactively to support users in text corpus analysis. We present a text analysis portal which displays inter-document similarity by means of so-called document maps, complemented by a display of the domain ontology and metadata-based access methods.

The identification and analysis of an enterprise's knowledge available in a documented form is a key element of knowledge management. Visual methods which allow easy access to a document collection's contents are an enabling technology. However, no single information retrieval technique is likely to adequately deal with such tasks independent of the specific situation. In this paper, we therefore present a visualization technique based on a modular approach that allows a variety of techniques from semantic document analysis to be used in the visualization of the structure of technical document collections.

Scenarios are valuable for supporting communication among system developers in the initial phases of requirements engineering. But the problem of how to fruitfully deal with large informal or semi-formal scenario collections consisting of weakly structured texts is still a key research issue. We report on a novel approach of automatically structuring textual document collections. We focus on its application to scenario management and discuss how structuring scenarios can help to support the analysis and maintenance of scenario collections. To evaluate the approach, we present a case study within a concrete software engineering effort.