A new pattern mining algorithm for analytics of real-time internet of things data

Abstract

The rise of IoT Real time data has led to new demands for mining systems to learn complex models with millions to billions of parameters, which promise adequate capacity to digest massive datasets and offer powerful predictive analytics. To support Big Data mining, high-performance powerful computing platforms are required, which impose regular designs to unleash the full power of the Big Data. Pattern mining poses a lot of interesting research problems and there are many areas that are still not well understood. The specifically very elementary challenges are to understand the meaningful data from the junk data that anticipated into the internet, refer as “Smart Data”. Eighty-five percent of the entire data are noisy or meaningless. It is a very tough often assigned to verify and separate to refine the data from the noisy junk. Researchers’ are proposing an algorithm of distributed pattern mining to give some sort of solution of the heterogeneity, scaling and hidden Big Data problems. The algorithm has evaluated in parameters like cost, speed, space and overhead. Researchers’ used IoT as the source of Big Data that generates heterogeneous Big Data. In this paper, we are representing the results of all tests proved that; the new method gives accurate results and valid outputs based on verifying them with the results of the other valid methods. Also, the results show that, the new method can handle the big datasets and decides the frequent pattern and produces the associate rule sets faster than that of the conventional methods and less amount of memory storage for processing. Overall the new method has a challenging performance as regard the memory storage and the speed of processing as compared to the conventional methods of frequent pattern mining like Apriori and FP-Growth techniques.