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Efficient Parallelism for Mining Sequential Rules in Time Series Data: A Lattice Based Approach*


Biplab Kumer Sarker, Kuniaki Uehara


Vol. 6  No. 7  pp. 137-143


A parallel algorithm based on a lattice theoretic approach is proposed to find the rules among patterns that sustain sequential nature in the multi-stream time series data. The human motion data captured by motion capturing system considered as multi-stream multidimensional data is used as real time data set. The data set is transformed into sequences of symbols of lower dimension due to its complex nature. The relations among symbols are expressed as “rules”. The proposed algorithm is implemented on a Distributed Shared Memory (DSM) multiprocessors system. The experimental results justify the efficiency of finding rules from the sequences of the patterns for time series data by achieving significant speed up comparing with the previous reported algorithm.


Data Mining, Time series data, Parallel algorithm, Association rule and Multiprocessor system