Exploiting Apache Flink's iteration capabilities for distributed Apriori: Community detection problem as an example
Sanjay Rathee, Arti Kashyap · 2016
Extraction of useful information from large datasets is one of the most important research problem. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exists many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these type of distributed applications, mapreduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software framework with mapreduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. But heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes hadoop inefficient. A number of map reduce based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted lot of attention because of their inbuilt support to distributed computations. Earlier we had proposed a reduced-Apriori algorithm on Spark platform which outperforms parallel Apriori, firstly because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, present work is a natural sequel of our earlier work and targets on implementing, testing and benchmarking Apriori on Apache Flink and compares it with Spark implementation. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori algorithm on Flink. We also use community detection graph mining problem as a test case to demonstrate our implementations.