However, large databases forces to partition the original database into chunks of data to process in main-memory.
Most current algorithms require as many database scans as the longest frequent sequences.
In this thesis work, we present ,dSpade, a parallel algorithm based on spade for discovering the set of all frequent sequences, targeting distributed-memory systems. In dSpade, horizontal database partitioning method is used, where each processor stores equal number of customer transactions. Each processor performs the same computation on its local data to get local support counts and broadcasts the results to other processors to find global frequent sequences during F1 and F2 computation. After discovering all F1 and F2, all frequent sequences are inserted into lattice to decompose the original problem into equivalence classes. Equivalence classes are mapped in a greedy heuristic to least loaded processors in a round-robin manner.