Mining association rules from multi-stream time series data on multiprocessor systems

Authors:
Biplab Kumer Sarker;Toshiya Hirata;Kuniaki Uehara;Virendra C. Bhavsar
Affiliations:
Faculty of Computer Science, University of New Brunswick, Fredericton, Canada;Graduate School of Science and Technology, Kobe University, Japan;Graduate School of Science and Technology, Kobe University, Japan;Faculty of Computer Science, University of New Brunswick, Fredericton, Canada
Venue:
ISPA'05 Proceedings of the Third international conference on Parallel and Distributed Processing and Applications
Year:
2005

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Abstract

Mining association rules from multi-stream data has received a lot of attention to the data mining community. It is quite effective and useful to discover such rules. However, it is a very time consuming and expensive task to mine the rules from these kinds of time ordered real valued continuous data sets with high dimensionality when they are enormous in size. This strongly motivates the need of efficient parallel processing techniques and algorithms. In this paper, we use parallel processing to discover dependency from the large amount of time series multi-stream data. We apply two parallel programming techniques (OpenMP and MPI) to implement this. The experimental results conducted in multiprocessor systems show the effectiveness of MPI over OpenMp.