Contributions in Merlin

In this paper, we present the Adora approach to object-oriented modeling of software (Adora stands for analysis and description of requirements and architecture). The main features of Adora that distinguish it from other approaches like UML are the use of abstract objects (instead of classes) as the basis of the model, a systematic hierarchical decomposition of the modeled system and the integration of all aspects of the system in one coherent model. The paper introduces the concepts of Adora and the rationale behind them, gives an overview of the language, sketches a novel concept for visualizing the model hierarchy with a tool and reports the results of a validation experiment for the Adora language.

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Elektronische Gruppenarbeitsräume und ihre Wirkungen waren Anfang der 90er Jahre das wichtigste Thema der amerikanischen Wirtschaftsinformatikforschung. Dennoch hat sich diese Technologie bisher noch nicht durchgesetzt. Der Beitrag führt zuerst am Beispiel des Koblenzer CSCW-Labors in elektronische Gruppenarbeitsräume ein und fasst ihre wesentlichenWirkungen und Potentiale zusammen. Der Hauptteil des Beitrags analysiert möglichen Ursache für die geringe Diffusion der Technologie und geht auf neuere Lösungsansätze ein. Dabei werden als Problembereiche behandelt: Stand der Technik, kritische Masse an Nutzern und Nutzungsgelegenheiten, Moderation und die Rolle des Moderators, kognitive Last und Einbettung in das organisatorische Umfeld sowie die physische Arbeitsumgebung.

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Thousands of business news stories (including press releases, earnings reports, general business news, etc.) are released each day. Recently, information technology advances have partially automated the processing of documents, reducing the amount of text that must be read. Current techniques (e.g., text classification and information extraction) for full-text analysis for the most part are limited to discovering information that can be found in single documents. Often, however, important information does not reside in a single document, but in the relationships between information distributed over multiple documents. This paper reports on an investigation into whether knowledge can be discovered automatically from relational data extracted from large corpora of business news stories. We use a combination of information extraction, network analysis, and statistical techniques. We show that relationally interlinked patterns distributed over multiple documents can indeed be extracted, and (specifically) that knowledge about companies’ interrelationships can be discovered. We evaluate the extracted relationships in several ways: we give a broad visualization of related companies, showing intuitive industry clusters; we use network analysis to ask who are the central players, and finally, we show that the extracted interrelationships can be used for important tasks, such as classifying companies by industry membership.