Abstract
The paper illustrates how to store and compute association sets of Big Transaction Data using Hadoop and HBase and then, shows the experimental result of a MapReduce algorithm using HBase to find out association in transaction data, which is a Market Basket Analysis algorithm of Association Rule in Business Intelligence. The algorithm sorts and converts the transaction data of HBase to data set with (key, value) pair, and stores the associated data to the HBase. The algorithm and HBase run on Amazon EC2 service using Apache Whirr. The experimental results show that the algorithm increases the performance as adding more nodes till a certain number of transaction data. However, it loses control and connection when there are too many IOs with more than 3.5 millions of transaction data in HBase.
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References
Woo, J., Xu, Y.: Market Basket Analysis Algorithm with Map/Reduce of Cloud Computing. In: The 2011 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2011), Las Vegas, July 18-21 (2011)
Woo, J., Basopia, S., Xu, Y., Kim, S.H.: Market Basket Analysis Algorithm with NoSQL DB HBase and Hadoop. In: The Third International Conference on Emerging Databases (EDB 2011), Songdo Park Hotel, Incheon, Korea, August 25-27 (2011)
Woo, J.: Apriori-Map/Reduce Algorithm. In: The 2012 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2012), Las Vegas, July 16-19 (2012)
Apache Hadoop Project, http://hadoop.apache.org/
Apache HBase, http://hbase.apache.org/
Apache Whirr, http://incubator.apache.org/whirr/
Lin, J., Dyer, C.: Data-Intensive Text Processing with MapReduce. Tutorial at the 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL HLT 2010), Los Angeles, California (June 2010)
Lin, J., Schatz, M.: Design Patterns for Efficient Graph Algorithms in MapReduce. In: Proceedings of the Eighth Workshop on Mining and Learning with Graphs Workshop (MLG-2010), Washington, D.C., pp. 78–85 (July 2010)
Lin, J., Dyer, C.: Data-Intensive Text Processing with MapReduce. Morgan & Claypool Publishers (2010)
Dean, J., Ghemawa, S.: MapReduce: Simplified Data Processing on Large Clusters. In: OSDI 2004, Google Labs, pp. 137–150 (2004)
Apache Zookeeper, http://zookeeper.apache.org
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© 2014 Springer-Verlag Berlin Heidelberg
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Woo, J., Lee, K. (2014). MapReduce Example with HBase for Association Rule. In: Park, J., Stojmenovic, I., Choi, M., Xhafa, F. (eds) Future Information Technology. Lecture Notes in Electrical Engineering, vol 276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40861-8_8
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DOI: https://doi.org/10.1007/978-3-642-40861-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40860-1
Online ISBN: 978-3-642-40861-8
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