2009, Schreck, Tobias, Bernard, Jürgen, von Landesberger, Tatiana, Kohlhammer, Jörn

Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Owing to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on several trajectory clustering problems, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.

2007, Schreck, Tobias, Tekusova, Tatiana, Kohlhammer, Jörn, Fellner, Dieter

Visual Analytics seeks to combine automatic data analysis with visualization and human-computer interaction facilities to solve analysis problems in applications characterized by occurrence of large amounts of complex data. The financial data analysis domain is a promising field for research and application of Visual Analytics technology, as it prototypically involves the analysis of large data volumes in solving complex analysis tasks.
We introduce a Visual Analytics system for supporting the analysis of large amounts of financial time-varying indicator data. A system, driven by the idea of extending standard technical chart analysis from one to two-dimensional indicator space, is developed. The system relies on an unsupervised clustering algorithm combined with an appropriately designed movement data visualization technique. Several analytical views on the full market and specific assets are offered for the user to navigate, to explore, and to analyze. The system includes automatic screening of the potentially large visualization space, preselecting possibly interesting candidate data views for presentation to the user. The system is applied to a large data set of time varying 2-D stock market data, demonstrating its effectiveness for visual analysis of financial data. We expect the proposed techniques to be beneficial in other application areas as well.

2007, Bustos Cárdenas, Benjamin Eugenio, Keim, Daniel A., Saupe, Dietmar, Schreck, Tobias

2006, Bustos Cárdenas, Benjamin Eugenio, Keim, Daniel A., Saupe, Dietmar, Schreck, Tobias, Vranić, Dejan V.

Methods for content-based similarity search are fundamental for managing large multimedia repositories, as they make it possible to conduct queries for similar content, and to organize the repositories into classes of similar objects. 3D objects are an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects, and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in 3D similarity search has arisen, and a growing number of competing algorithms for the retrieval of 3D objects have been proposed. The contributions of this paper are to survey a body of recently proposed methods for 3D similarity search, to organize them along a descriptor extraction process model, and to present an extensive experimental effectiveness and efficiency evaluation of these methods, using several 3D databases.

2007, Bustos Cárdenas, Benjamin Eugenio, Keim, Daniel A., Saupe, Dietmar, Schreck, Tobias, Tatu, Andrada

3D Objekte sind ein wichtiger Typ Multimedia Daten mit einer Reihe vielversprechender Anwendungsmöglichkeiten etwa in der industriellen Produktion, in Simulation, Unterhaltung und Visualisierung. Die Definition von Ähnlichkeit zwischen 3D Objekten und die Implementierung von entsprechenden Ähnlichkeitssuchalgorithmen sind interessant für den Einsatz in 3D-Datenbanksystemen, repräsentieren aber gleichzeitig schwierige Probleme. In dieser Arbeit stellen wir Methoden dar, um effektives Retrieval in 3D-Datenbanken zu realisieren. Wir besprechen zudem Methoden, um Ergebnisse von Ähnlichkeitssuchanfragen sowie ganze 3D Objekträume visuell zu analysieren.

2005, Bustos Cárdenas, Benjamin Eugenio, Keim, Daniel A., Saupe, Dietmar, Schreck, Tobias, Vranić, Dejan V.

The development of effective content-based multimedia search systems is an important research issue, due to the growing amount of digital audio-visual information. In the case of images and video, the growth of digital data has been observed since the introduction of 2D capture devices. A similar development is expected for 3D data, as acquisition and dissemination technology of 3D models is constantly improving. 3D objects are becoming an important type of multimedia data, with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects, and designing algorithms that implement such similarity definitions, is a difficult problem. Over the last few years, a strong interest in methods for 3D similarity search has arisen, and a growing number of competing algorithms for content-based retrieval of 3D objects have been proposed. We survey feature-based methods for 3D retrieval, and we propose a taxonomy for these methods. We also present experimental results, comparing the effectiveness of some of the surveyed methods.