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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© 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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