Scalable and parallel sequential pattern mining using spark

The performance of the existing parallel sequential pattern mining algorithms is often unsatisfactory due to high IO overhead and imbalanced load among the computing nodes. To address such problems, this paper proposes two efficient parallel sequential pattern mining algorithms based on Spark, i.e., GSP-S (GSP algorithm based on Spark) and PrefixSpan-S (PrefixSpan algorithm based on Spark). For both algorithms, multiple MapReduce jobs are implemented to complete a mining task. To reduce IO overhead and take advantage of cluster memory, the first MapReduce job loads sequence database from the Hadoop Distributed File System (HDFS) into the Spark resilient distributed datasets (RDDs), and further MapReduce jobs read the database from the RDDs and store intermediate results back into the RDDs. Our findings suggest that a wise choice can be made between GSP-S and PrefixSpan-S, depending on the user-specified minimum support threshold. Moreover, theoretical analysis shows that GSP-S and PrefixSpan-S are sensitive to data distribution on the cluster. To further improve performance, we propose two database partition strategies to balance load among the computing nodes in a cluster. Experiment results demonstrate the high performance of GSP-S and PrefixSpan-S in terms of load-balancing, speedup and scalability.