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17th International Conference on Database and Expert Systems Applications (DEXA'06)
Extracting Partition Statistics from Semistructured Data
Krakow, Poland
September 04-September 08
ISBN: 0-7695-2641-1
John N. Wilson, University of Strathclyde, UK
Richard Gourlay, University of Strathclyde, UK
Robert Japp, University of Strathclyde, UK
Mathias Neumuller, University of Strathclyde, UK
The effective grouping, or partitioning, of semistructured data is of fundamental importance when providing support for queries. Partitions allow items within the data set that share common structural properties to be identified efficiently. This allows queries that make use of these properties, such as branching path expressions, to be accelerated. Here, we evaluate the effectiveness of several partitioning techniques by establishing the number of partitions that each scheme can identify over a given data set. In particular, we explore the use of parameterised indexes, based upon the notion of forward and backward bisimilarity, as a means of partitioning semistructured data; demonstrating that even restricted instances of such indexes can be used to identify the majority of relevant partitions in the data.
Citation:
John N. Wilson, Richard Gourlay, Robert Japp, Mathias Neumuller, "Extracting Partition Statistics from Semistructured Data," dexa, pp.497-501, 17th International Conference on Database and Expert Systems Applications (DEXA'06), 2006
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