Big Data Frequent Pattern Mining - Frequent Pattern Mining - page 252

their work with idling ones or not. Another open problem is that of mining sub-
patterns in a large object, where sub-patterns can span multiple process' data. Current
methods for sequence motif mining and frequent subgraph mining in a large graph
either rely on maximum pattern length constraints that allow each process to store
overlapping data partition boundaries or transfer data partitions amongst all processes
during each iteration of the algorithm. Neither solution scales when presented with
Big Data, calling for efficient methods to solve this problem exactly.

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