Discovery of sequential patterns is becoming increasingly useful and essential in many scientific and commercial domains. Enormous sizes of available datasets and possibly large number of mined patterns demand efficient, scalable, and parallel algorithms. Even though a number of algorithms have been developed to efficiently parallelize frequent pattern discovery algorithms that are based on the candidate-generation-and-counting framework, the problem of parallelizing the more efficient projection-based algorithms has received relatively little attention and existing parallel formulations have been targeted only toward shared-memory architectures. The irregular and unstructured nature of the task-graph generated by these algorithms and the fact that these tasks operate on overlapping sub-databases makes it challenging to efficiently parallelize these algorithms on scalable distributed-memory parallel computing architectures. In this paper we present and study a variety of distributed-memory parallel algorithms for a tree-projection-based frequent sequence discovery algorithm that are able to minimize the various overheads associated with load imbalance, database overlap, and interprocessor communication. Our experimental evaluation on a 32 processor IBM SP show that these algorithms are capable of achieving good speedups, substantially reducing the amount of the required work to find sequential patterns in large databases.
Bibliographical noteFunding Information:
This work was supported in part by NSF CCR-9972519, EIA-9986042, ACI-9982274, ACI-0133464, and ACI-0312828; the Digital Technology Center at the University of Minnesota; and by the Army High Performance Computing Research Center (AHPCRC) under the auspices of the Department of the Army, Army Research Laboratory (ARL) under Cooperative Agreement number DAAD19-01-2-0014. The content of which does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute.
- Data mining
- Database projection algorithms
- Frequent sequential patterns