http://dl.gi.de/handle/20.500.12116/31795 2026-07-23T15:04:09Z 2026-07-23T15:04:09Z Böhm, Christian Kunath, Peter Pryakhin, Alexey Schubert, Matthias http://dl.gi.de/handle/20.500.12116/31833 2020-02-11T13:22:16Z 2007-01-01T00:00:00Z

Effective and Efficient Indexing for Large Video Databases Böhm, Christian; Kunath, Peter; Pryakhin, Alexey; Schubert, Matthias Kemper, Alfons; Schöning, Harald; Rose, Thomas; Jarke, Matthias; Seidl, Thomas; Quix, Christoph; Brochhaus, Christoph Content based multimedia retrieval is an important topic in database systems. An emerging and challenging topic in this area is the content based search in video data. A video clip can be considered as a sequence of images or frames. Since this representation is too complex to facilitate efficient video retrieval, a video clip is often summarized by a more concise feature representation. In this paper, we transform a video clip into a set of probabilistic feature vectors (pfvs). In our case, a pfv corresponds to a Gaussian in the feature space of frames. We demonstrate that this representation is well suited for accurate video retrieval. The use of pfvs allows us to calculate confidence values for frames or sets of frames for being contained within a given video in the database. These confidence values can be employed to specify two types of queries. The first type of query retrieves the videos stored in the database which contain a given set of frames with a probability that is larger than a given thresh-old value. Furthermore, we introduce a probabilistic ranking query retrieving the k database videos which contain the given query set with the highest probabilities. To efficiently process these queries, we introduce query algorithms on set-valued objects. Our solution is based on the Gauss-tree, an index structure for efficiently managing Gaussians in arbitrary vector spaces. Our experimental evaluation demonstrates that sets of probabilistic feature vectors yield a compact and descriptive representation of video clips. Additionally, we show that our new query algorithms outperform competitive approaches when answering the given types of queries on a database of over 900 real world video clips.

2007-01-01T00:00:00Z Mansmann, Svetlana Mansmann, Florian Scholl, Marc H. Keim, Daniel A. http://dl.gi.de/handle/20.500.12116/31831 2020-02-11T13:22:15Z 2007-01-01T00:00:00Z

Hierarchy-driven Visual Exploration of Multidimensional Data Cubes Mansmann, Svetlana; Mansmann, Florian; Scholl, Marc H.; Keim, Daniel A. Kemper, Alfons; Schöning, Harald; Rose, Thomas; Jarke, Matthias; Seidl, Thomas; Quix, Christoph; Brochhaus, Christoph Analysts interact with OLAP data in a predominantly “drill-down” fashion, i.e. gradually descending from a coarsely grained overview towards the desired level of detail. Analysis tools enable visual exploration as a sequence of navigation steps in the data cubes and their dimensional hierarchies. However, most state-of-the-art solutions are limited either in their capacity to handle complex multidimensional data or in the ability of their visual metaphors to provide an overview+details context. This work proposes an explorative framework for OLAP data based on a simple but powerful approach to analyzing data cubes of virtually arbitrary complexity. The data is queried using an intuitive navigation in which each dimension is represented by its hierarchy schema. Any granularity level can be dragged into the visualization to serve as an disaggregation axis. The results of the iterative exploration are mapped to a specified visualization technique. We favor hierarchical layouts for their natural ability to show step-wise decomposition of aggregate values. The power of the tool to support various application scenarios is demonstrated by presenting use cases from different domains and the visualization techniques suitable for solving specific analysis tasks.

2007-01-01T00:00:00Z Beck, Matthias Radde, Sven Freitag, Burkhard http://dl.gi.de/handle/20.500.12116/31830 2020-02-11T13:22:15Z 2007-01-01T00:00:00Z

Ranking von Produktempfehlungen mit präferenz-annotiertem SQL Beck, Matthias; Radde, Sven; Freitag, Burkhard Kemper, Alfons; Schöning, Harald; Rose, Thomas; Jarke, Matthias; Seidl, Thomas; Quix, Christoph; Brochhaus, Christoph Web-basierte, datenbankgestützte Beratungssysteme finden durch zentrale Wartbarkeit bei sich permanent ändernden Produktpaletten aktuell starke Verbreitung. Kernpunkt für eine optimale Produktempfehlung ist dabei die Berücksichtigung der Präferenzen des Kunden, welche auf eine möglichst einfache und nachvollziehbare Art und Weise spezifiziert werden sollten. Daher wird ein Ansatz präsentiert, der erlaubt, die vom Benutzer ohnehin anzugebenden Selektionsbedingungen zusätzlich mit Gewichten zu annotieren und damit die Sortierung der Empfehlungen zu beeinflussen. Dies wird durch eine erweiterte SQL-Syntax ermöglicht, über die theoretisch fundiert ein Ranking auf der Ergebnismenge definiert wird.

2007-01-01T00:00:00Z Graefe, Goetz http://dl.gi.de/handle/20.500.12116/31832 2020-02-11T13:22:15Z 2007-01-01T00:00:00Z

Algorithms for merged indexes Graefe, Goetz Kemper, Alfons; Schöning, Harald; Rose, Thomas; Jarke, Matthias; Seidl, Thomas; Quix, Christoph; Brochhaus, Christoph

2007-01-01T00:00:00Z