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In this paper, a new pattern-growth algorithm is presented to mine frequent sequential patterns using First-Occurrence Forests (FOF). This algorithm uses a simple list of pointers to the first-occurrences of a symbol in the aggregate tree [1], as the basic data structure for database representation, and does not rebuild aggregate trees for projection databases. The experimental evaluation shows that our new FOF mining algorithm outperforms the PLWAP-tree mining algorithm [2] and the FLWAP-tree mining algorithm [3], both in the mining time and the amount of memory used.

This paper defines probabilistic support and probabilistic frequent closed itemsets in uncertain databases for the first time. It also proposes a probabilistic frequent closed itemset mining (PFCIM) algorithm to mine probabilistic frequent closed itemsets from uncertain databases.

If the loop iterations of a loop nest cannot be partitioned into independent sets, the data communication for data dependences are inevitable in order to execute them on parallel machines. This kind of loop nests are referred to as Doacross loop nests. This paper is concerned with compiler algorithms for parallelizing Doacross loop nests for distributed-memory multicomputers. We present a method that combines loop tiling, chain-based scheduling and indirect message passing to generate e cient message-passing parallel codes. We present our experiment results on Fujitsu AP1000 which show that low communication overhead and high speedup for Doacross loop nests on multicomputers can be achieved by tuning these techniques.

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