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Cloud-Based Infrastructure for Data-Intensive e-Science Applications: Requirements and Architecture
Published in Olivier Terzo, Lorenzo Mossucca, Cloud Computing with e-Science Applications, 2017
Yuri Demchenko, Canh Ngo, Paola Grosso, de Laat Cees, Peter Membrey
Big data analytics tools are currently offered by the major cloud services providers, such as Amazon Elastic MapReduce and Dynamo [41], Microsoft Azure HDInsight [42], IBM Big Data Analytics [43]. HPCC Systems by LexisNexis [44], Scalable Hadoop, and data analytics tools services are offered by a few companies that position themselves as big data companies, such as Cloudera [45] and a few others [46].
Big Data Classification Using Enhanced Dynamic KPCA and Convolutional Multi-Layer Bi-LSTM Network
Published in IETE Journal of Research, 2023
Companies are improving their infrastructure and applying “Big Data technologies” capability to forecast what will emerge from the varying loads. Big Data technologies such as Spark, Hadoop, SAP-HANA, High-Performance Cluster Computing (HPCC), and others are available; however, Hadoop is the most widely utilized. The useful information source Big Data is too large for traditional database systems to handle, so it faces challenge while changing it into a form that fit quickly into the present database structures [3]. There must be another way to process these data by which it can extract the values. It is important to remember that there are no denotative criteria for how “big” a data set must be labeled as Big Data [4]. Big data can be characterized by veracity, volume, value, velocity, and variety, it is defined by the complex process of storing, retrieving, and processing unstructured, semi-structured, and structured data that can be mined using appropriate algorithms to extract useful information to help people make better decisions.