By Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao
This booklet has a suite of articles written via great facts specialists to explain a number of the state of the art equipment and purposes from their respective components of curiosity, and offers the reader with an in depth review of the sector of massive facts Analytics because it is practiced this day. The chapters hide technical points of key components that generate and use massive information resembling administration and finance; medication and healthcare; genome, cytome and microbiome; graphs and networks; web of items; monstrous info criteria; bench-marking of platforms; and others. as well as assorted functions, key algorithmic ways reminiscent of graph partitioning, clustering and finite blend modelling of high-dimensional info also are coated. the numerous selection of issues during this quantity introduces the reader to the richness of the rising box of massive information Analytics.
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Additional resources for Big Data Analytics: Methods and Applications
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In Selective data replication, intermediate data generated by the Big data application are replicated on the same server where they were generated. For example, in the case of Map phase failures in a chained MapReduce job, the aﬀected Map task can be restarted directly, if the intermediate data from previous Reduce tasks were available on the same machine. The Selective replication scheme reduces the need for replication in the Map phase. However, it is not eﬀective in Reduce phase, since the Reduce data are mostly consumed locally.
It all began in the early 1980s with ‘standard reports’ that described what had happened to a business function within a speciﬁed time period. These reports could be information about how many transactions took place in a branch, how much quantity of a product was sold, what was the amount spent on a certain stock keeping unit (SKU) by the loyalty card holders of a retailer, etc. The reports had to be preprogrammed and were produced at speciﬁed times such as quarterly, half-yearly or annually. With the advent of relational database systems (RDBMS) and the structured query language (SQL) ad-hoc reporting became possible.
Big Data Analytics: Methods and Applications by Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao