Big Data Mining Platforms- Distributed Aggregation for Data-Parallel Computing

Abstract

This paper presents the Big Data Mining platforms for parallel computing. Big Data is concerned with large-volume, complex, growing data sets with multiple, autonomous sources, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. In typical data mining systems, the mining procedures require computational intensive computing units for data analysis and comparisons. A computing platform is needed to have efficient access to at least two fypes of resources: they are data and computing processors. For small scale data mining tasks, a single desktop computer, which contains hard disk and CPU processors, is sufficient to fulfill the data mining goals. Indeed, many data mining algorithms are designed for this fype of problem settings. For medium scale data mining tasks, data are fyp/cally large [and possibly distributed) and cannot be flt Into the main memory. Common solutions rely on parallel computing, collective mining to sample and aggregate data from different sources and then use parallel computing programming. In this paper, the authors have concentrated on the Tier I I.e., Big Data Mining Platforms by using MapReduce[MRJ. For this technique the authors follow Distributed Aggregation for Data Parallel computing. Through this technique, there is reduction of network traffic over the network. Keywords: Big Data, Big Data Platform, Data Mining, Distributed Aggregation, MapReduce[MRJ mains, including physical, biological and biomedical sciences.