(Georgia Institute of Technology, 1995)
Omiecinski, Edward; Navathe, Shamkant B.; Achyutuni, Kiran Jyotsna
In this paper, we study the data placement problem from a reorganization
point of view. Effective placement of the declustered fragments of a
relation is crucial to the performance of parallel database systems
having multiple disks. Given the dynamic nature of database systems,
the optimal placement of fragments will change over time
and this will necessitate a reorganization in order to maintain
the performance of the database system at acceptable levels.
This study shows that the choice of a data placement strategy can
have a significant impact on the reorganization costs.
Up until now, data placement heuristics were designed with the
principal purpose of balancing the load. However, this paper
shows that such a policy can be beneficial only in the short term.
Long term database designs should take reorganization
costs into consideration while making design choices.
(Georgia Institute of Technology, 1995)
Omiecinski, Edward; Navathe, Shamkant B.; Savasere, Ashok
Mining for association rules between items in a large database of sales transactions has been described as an important database mining problem. In this paper we present an efficient algorithm for mining association rules that is fundamentally different from known algorithms.
Compared to the previous algorithms, our algorithm reduces
both CPU and I/O overheads. In our experimental study it
was found that for large databases, the CPU overhead was
reduced by as much as a factor of seven and I/O was reduced
by almost an order of magnitude. Hence this algorithm is
especially suitable for very large size databases. The
algorithm is also ideally suited for parallelization. We
have performed extensive experiments and compared the
performance of the algorithm with one of the best existing
algorithms.