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Kai-Uwe Sattler

Researcher at Technische Universität Ilmenau

Publications -  288
Citations -  3136

Kai-Uwe Sattler is an academic researcher from Technische Universität Ilmenau. The author has contributed to research in topics: Computer science & Query optimization. The author has an hindex of 26, co-authored 259 publications receiving 2910 citations. Previous affiliations of Kai-Uwe Sattler include Otto-von-Guericke University Magdeburg.

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Proceedings ArticleDOI

Data summaries for on-demand queries over linked data

TL;DR: An approximate index structure summarising graph-structured content of sources adhering to Linked data principles is developed, an algorithm for answering conjunctive queries over Linked Data on theWeb exploiting the source summary is provided, and the system is evaluated using synthetically generated queries.
Proceedings ArticleDOI

The mixed workload CH-benCHmark

TL;DR: The definition of a new, complex, mixed workload benchmark, called mixed workload CH-benCHmark, which bridges the gap between the established single-workload suites of TPC-C for OLTP and T PC-H for OLAP, and executes a complex mixed workload.
Journal ArticleDOI

Report on the Dagstuhl Seminar

TL;DR: The objective of the Dagstuhl Seminar was to foster collaboration among researchers that deal with DQ in different areas, assess existing results in managing the quality of data, and establish a framework for future research in the area of DQ.
BookDOI

Current trends in database technology -- EDBT 2006 : EDBT 2006 Workshops PhD, DataX, IIDB, IIHA, ICSNW, QLQP, PIM, PaRMA, and Reactivity on the Web, Munich, Germany, March 26-31, 2006 : revised selected papers

TL;DR: This presentation discusses Scalable Continuous Query Processing and Moving Object Indexing in Spatio-temporal Databases, and Managing Valid Time Semantics for Semistructured Multimedia Clinical Data.
Journal ArticleDOI

Efficient similarity-based operations for data integration

TL;DR: A similarity-based variants of grouping and join operators that produces groups of similar tuples, the extended join combines tuples satisfying a given similarity condition is presented.