MapReduce Framework to Improve the Efficiency of Large Scale Item Sets in IoT Using Parallel Mining of Representative Patterns in Big Data

Abstract

With the coming of the time of huge information, individuals can gather rich and various information from a wide assortment of assortment gadgets, like those of the Internet of Things. Information concealed in enormous information is extremely helpful and important. Frequent pattern mining, as a basic method of data mining, is applied to every aspect of society. However, the application of traditional frequent pattern mining methods to big data involves bottlenecks due to the large number of result sets. Such bottlenecks make it challenging to deliver commonsense worth underway and life. We proposed a new approach which involved representative patterns using mining technique. This framework can make the runtime difficult to evaluate in large data environments. Our approach gives best solution for the above gaps with the help of online representative pattern-set parallel-mining algorithm. We used MapReduce framework which performs horizontal segmentation over data bases. Finally, several performance optimization strategies are proposed. As shown by numerous experiments on the actual dataset, the algorithm proposed in this paper improves the time efficiency by one order of magnitude. Several optimization strategies reduce the execution time to varying degrees.