MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems

作者：

Highlights：

• MRQAR is a parallel framework to find quantitative association rules in Big Data.

• MRQAR is based on the MapReduce paradigm and implemented using Apache Spark.

• MRQAR can execute any sequential quantitative association rule algorithm.

• MRQAR uses the MOPNAR algorithm as particular case study.

• MRQAR discovers high quality quantitative association rules in Big Data.

摘要

•MRQAR is a parallel framework to find quantitative association rules in Big Data.•MRQAR is based on the MapReduce paradigm and implemented using Apache Spark.•MRQAR can execute any sequential quantitative association rule algorithm.•MRQAR uses the MOPNAR algorithm as particular case study.•MRQAR discovers high quality quantitative association rules in Big Data.

论文关键词：Quantitative association rules,Multiobjective evolutionary algorithms,Big Data,MapReduce,Spark

论文评审过程：Received 27 September 2017, Revised 27 April 2018, Accepted 28 April 2018, Available online 30 April 2018, Version of Record 11 May 2018.

论文官网地址：https://doi.org/10.1016/j.knosys.2018.04.037