Ralf Rantzau

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Contextual preferences take the form that item i1 is preferred to item <i>i</i><sub>2</sub> in the context of <i>X</i>. For example, a preference might state the choice for Nicole Kidman over Penelope Cruz in drama movies, whereas another preference might choose Penelope Cruz over Nicole Kidman in the context of Spanish dramas. Various sources provide(More)
We introduce an auditing framework for determining whether a database system is adhering to its data disclosure policies. Users formulate audit expressions to specify the (sensitive) data subject to disclosure review. An audit component accepts audit expressions and returns all queries (deemed “suspicious”) that accessed the specified data during their(More)
The EPCglobal consortium defines standards to enable data sharing of electronic product code related information within and between enterprises, which typically comprises events of RFID readers as well as product information about the tagged products. An EPCglobal network consists of nodes, each of which may have complex data usage and sharing policies and(More)
SQL-based data mining algorithms are rarely used in practice today. Most performance experiments have shown that SQL-based approaches are inferior to main-memory algorithms. Nevertheless, database vendors try to integrate analysis functionalities to some extent into their query execution and optimization components in order to narrow the gap between data(More)
Queries containing universal quantification are used in many applications, including business intelligence applications. Several algorithms have been proposed to implement universal quantification efficiently. These algorithms are presented in an isolated manner in the research literature – typically, no relationships are shown between them. Furthermore,(More)
  • Ralf Rantzau
  • Database Support for Data Mining Applications
  • 2004
Algorithms for finding frequent itemsets fall into two broad classes: (1) algorithms that are based on non-trivial SQL statements to query and update a database, and (2) algorithms that employ sophisticated in-memory data structures, where the data is stored into and retrieved from flat files. Most performance experiments have shown that SQL-based(More)
Today many applications routinely generate large quantities of data. The data often takes the form of (time) series, or more generally streams, i.e. an ordered sequence of records. Analysis of this data requires stream processing techniques which differ in significant ways from what current database analysis and query techniques have been optimized for. In(More)