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| creator |
Rantzau, Ralf
| | date |
2002-03
| | | | description |
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 approaches are inferior to main-memory
algorithms. However, the current trend of database vendors to
integrate analysis functionalities into their query execution and
optimization components, i.e., "closer to the data,"
suggests revisiting these results and searching for new, potentially
better solutions.
We investigate approaches based on SQL-92 and present a new approach
called Quiver that employs universal and existential
quantifications. This approach uses a table layout for itemsets,
where a group of multiple records represents a single itemset.
Hence, our vertical layout is similar to the popular layout used for
the transaction table, which is the input of frequent itemset
discovery. Our approach is particularly beneficial if the database
system in use provides adequate strategies and techniques for
processing universally quantified queries, unlike current commercial
systems.
| | format |
application/pdf | |