Research And Implementation Of Association Rules Algorithm In Railway Industry Based On Big Data

The study on high speed railway has been done a lot in recent and past decades.High speed railway provides services to transport from one end to another.Apart from this,its safety and efficiency can never be neglected.To make anything more accurate we generally try to overcome its failure and find out the related factor and issues.To do this,there are so many traditional and latest technology,but Big Data and its technology become more highlighted these days because it can handle huge amount of data easily.So big data are also popular in the railroad industry to analysis data,there are many challenges which need to be addressed but this paper covers failure data analyzation.This paper intends to analyze a failure dataset of railway industry by implementing the association rules.Association rules are used to find the likelihood of the co-event of things in a data set.Searching frequent patterns in transaction databases are considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task.Finding failure patterns from a large volume of dataset becomes a challenging task.Based on Hadoop Apache Spark we implement an Apriori algorithm,Apache Spark is an parallel processing framework for running large-scale data analytics application.It can process data from HDFS and support in-memory processing.The results of study exhibit that this algorithm can scale well and productively process large data sets on commodity hardware.Excavation potential value of the data through the big data technology and method will help to optimize the management of the railway industry.It provides;make the journey safe without any disruption,enable decision-making easy,data management becomes fast and accurate.