LAUSR

The era of petabyte data has arrived as the digital big data universe continues its expansion toward exascale with massive volumes of data generated by diverse distributed sources. The size of big data makes it very difficult to gain insight into the meaning of data. In industrial applications, in order to explore both the meaning of data and the complex relationship between data components, big data needs to be processed and reduced enabling further deeper analysis in a timely manner. In this article an integrated data analytics framework is presented designed to extract the set of instances exhibiting statistical dependency from the massive volume of data in a pre-defined quasi real-time manner. The parallel computing model of MapReduce is enhanced to realize Magnet. The solution presented in this article is applicable to the telecommunications market where it optimizes next-generation network management systems for heterogeneous radio access technologies.