We study extensions of description logics from the widely used EL family with operators that make it possible to speak about different levels of abstraction. We analyze the computational complexity of reasoning and show that often, this complexity is significantly lower than in the corresponding extension of the more expressive description logic ALC. By slightly varying the semantics, we also obtain a case that admits reasoning in polynomial time.

Ontologies often require knowledge representation on multiple levels of abstraction, but description logics (DLs) are not well-equipped for supporting this. We propose an extension of DLs in which abstraction levels are first-class citizens and which provides explicit operators for the abstraction and refinement of concepts and roles across multiple abstraction levels, based on conjunctive queries. We prove that reasoning in the resulting family of DLs is decidable while several seemingly harmless variations turn out to be undecidable. We also pinpoint the precise complexity of our logics and several relevant fragments.

Data proling is the discipline of examining an unknown dataset for its structure and statistical information. It is a preprocessing step in a wide range of applications, such as data integration, data cleansing, or query optimization. For this reason, many algorithms have been proposed for the discovery of dierent kinds of metadata. When analyzing a dataset, these proling algorithms are often applied in sequence, but they do not support one another, for instance, by sharing I/O cost or pruning information. We present the holistic algorithm Muds, which jointly discovers the three most important metadata: inclusion dependencies, unique column combinations, and functional dependencies. By sharing I/O cost and data structures across the dierent discovery tasks, Muds can clearly increase the eciency of traditional sequential data proling. The algorithm also introduces novel inter-task pruning rules that build upon dierent types of metadata, e.g., unique column combinations to infer functional dependencies. We evaluate Muds in detail and compare it against the sequential execution of state-of-the-art algorithms. A comprehensive evaluation shows that our holistic algorithm outperforms the baseline by up to factor 48 on datasets with favorable pruning conditions.

A lot of research efforts are going on in the area of mining emotions within the world wide web. The BlogIntelligence application is analyzing tons of blog posts and extracts emotions out of this big amount of data. Therefore we thought about how to visualize these emotions in a very meaningful way. While we applied a smart map as a proven technique, we overcame conceptual and technical challenges to provide a feasible utility.