Stefan Hildenbrand

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Column-oriented database systems [19, 23] perform better than traditional row-oriented database systems on analytical workloads such as those found in decision support and business intelligence applications. Moreover, recent work [1, 24] has shown that lightweight compression schemes significantly improve the query processing performance of these systems.(More)
Modern database systems employ Snapshot Isolation to implement concurrency control and isolationbecause it promises superior query performance compared to lock-based alternatives. Furthermore, Snapshot Isolation never blocks readers, which is an important property for modern information systems, which have mixed workloads of heavy OLAP queries and short(More)
Recent studies have shown that column stores can outperform row stores significantly. This paper explores alternative approaches to extend column stores with versioning, i.e., time travel queries and the maintenance of historic data. On the one hand, adding versioning can actually simplify the design of a column store because it provides a solution for the(More)
The amount of data available today is huge and keeps increasing steadily. Databases help to cope with huge amounts of data. Yet, traditional databases are not fast enough to answer the complex analytical queries that decision makers in big enterprises ask over large datasets. This is where column stores have their field of application. Tailored to this type(More)
In der heutigen Zeit der Einführung neuer Technologien der Funkübertragung, der Mobilfunkkommunikation ( D-Netz, E-Netz, UMTS ), der Netzwerke, Bussysteme ( CAN, LON, INTERBUS ) , und Video/ Audio Anwendungen werden neue Übertragungsraten und hohe Taktfrequenzen genutzt. Für bisherige technische Anwendungen mit Frequenzobergrenzen bis 1000 MHz [1] waren(More)
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