By Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao
This booklet has a set of articles written by way of immense facts specialists to explain many of the state of the art tools and purposes from their respective parts of curiosity, and gives the reader with a close evaluate of the sector of massive info Analytics because it is practiced at the present time. The chapters hide technical elements of key parts that generate and use titanic facts corresponding to administration and finance; drugs and healthcare; genome, cytome and microbiome; graphs and networks; net of items; immense facts criteria; bench-marking of structures; and others. as well as assorted functions, key algorithmic techniques equivalent to graph partitioning, clustering and finite combination modelling of high-dimensional facts also are lined. the numerous selection of topics during this quantity introduces the reader to the richness of the rising box of huge facts Analytics.
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Extra info for Big Data Analytics: Methods and Applications
Major components of an HPCC system include a Thor cluster and a Roxie cluster, although the latter is optional. com. K. Pusala et al. (ETL), as well as linking and indexing massive data from diﬀerent sources. Roxie is called the query cluster, which is responsible for delivering data for online queries and online analytical processing (OLAP). Similar to Hadoop, HPCC also uses a distributed ﬁle system to support parallel processing on Big data. However, compared with HDFS, the distributed ﬁle system used by HPCC has some signiﬁcant distinctions.
To handle such computations on large graphs, the prediction system is developed by using the MapReduce framework and a Hadoop cluster environment. We implemented the MapReduce functions to extract the medical concepts from millions of publications in Medline dataset, to generate the labeled data, and to extract structural features from the large concept graph. Implementing such a graph computation method on MapReduce framework has its own limitations. One of the drawbacks of MapReduce framework is its inability to retain the state of a graph across multiple iterations .
Accessed 28 Feb 2015 28. Jayasimha K, Rajyashree M, Tolga K (2013) Large-scale recommendations in a dynamic marketplace. In Workshop on large scale recommendation systems at RecSys 13: 29. Jayasimha K, Rajyashree M, Tolga K (2015) Subjective similarity: personalizing alternative item recommendations. In: WWW workshop: Ad targeting at scale 30. Katukuri JR, Xie Y, Raghavan VV, Gupta A (2012) Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks.