|
| creator |
Rantzau, Ralf
| | Shapiro, Leonard
| | Mitschang, Bernhard
| | Wang, Quan
| | date |
2003-01
| | | | description |
Queries containing universal quantification are used in many
applications, including business intelligence applications and in
particular data mining. We present a comprehensive survey of the
structure and performance of algorithms for universal
quantification. We introduce a framework that results in a complete
classification of input data for universal quantification. Then we
go on to identify the most efficient algorithm for each such class.
One of the input data classes has not been covered so far. For this
class, we propose several new algorithms. Thus, for the first time,
we are able to identify the optimal algorithm to use for any given
input dataset.
These two classifications of optimal algorithms and input data are
important for query optimization. They allow a query optimizer to
make the best selection when optimizing at intermediate steps for
the quantification problem.
In addition to the classification, we show the relationship between
relational division and the set containment join and we illustrate
the usefulness of employing universal quantifications by presenting
a novel approach for frequent itemset discovery.
| | format |
application/pdf | |