http://dl.gi.de/handle/20.500.12116/11553 Sun, 26 Jul 2026 15:24:55 GMT 2026-07-26T15:24:55Z http://dl.gi.de/handle/20.500.12116/11768 ADAMpro: Database Support for Big Multimedia Retrieval Giangreco, Ivan; Schuldt, Heiko For supporting retrieval tasks within large multimedia collections, not only the sheer size of data but also the complexity of data and their associated metadata pose a challenge. Applications that have to deal with big multimedia collections need to manage the volume of data and to effectively and efficiently search within these data. When providing similarity search, a multimedia retrieval system has to consider the actual multimedia content, the corresponding structured metadata (e.g., content author, creation date, etc.) and—for providing similarity queries—the extracted low-level features stored as densely populated high-dimensional feature vectors. In this paper, we present ADAMpro, a combined database and information retrieval system that is particularly tailored to big multimedia collections. ADAMpro follows a modular architecture for storing structured metadata, as well as the extracted feature vectors and it provides various index structures, i.e., Locality-Sensitive Hashing, Spectral Hashing, and the VA-File, for a fast retrieval in the context of a similarity search. Since similarity queries are often long-running, ADAMpro supports progressive queries that provide the user with streaming result lists by returning (possibly imprecise) results as soon as they become available. We provide the results of an evaluation of ADAMpro on the basis of several collection sizes up to 50 million entries and feature vectors with different numbers of dimensions. Fri, 01 Jan 2016 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11768 2016-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11766 Editorial Hagen, Matthias; Stein, Benno; Härder, Theo Fri, 01 Jan 2016 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11766 2016-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11761 News Schenkel, Ralf Fri, 01 Jan 2016 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11761 2016-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11771 Scalable DB+IR Technology: Processing Probabilistic Datalog with HySpirit Frommholz, Ingo; Roelleke, Thomas Probabilistic Datalog (PDatalog, proposed in 1995) is a probabilistic variant of Datalog and a nice conceptual idea to model Information Retrieval in a logical, rule-based programming paradigm. Making PDatalog work in real-world applications requires more than probabilistic facts and rules, and the semantics associated with the evaluation of the programs. We report in this paper some of the key features of the HySpirit system required to scale the execution of PDatalog programs.Firstly, there is the requirement to express probability estimation in PDatalog. Secondly, fuzzy-like predicates are required to model vague predicates (e.g. vague match of attributes such as age or price). Thirdly, to handle large data sets there are scalability issues to be addressed, and therefore, HySpirit provides probabilistic relational indexes and parallel and distributed processing. The main contribution of this paper is a consolidated view on the methods of the HySpirit system to make PDatalog applicable in real-scale applications that involve a wide range of requirements typical for data (information) management and analysis. Fri, 01 Jan 2016 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11771 2016-01-01T00:00:00Z