By Borko Furht, Flavio Villanustre
The target of this booklet is to introduce the elemental recommendations of massive information computing after which to explain the full answer of huge facts difficulties utilizing HPCC, an open-source computing platform.
The e-book includes 15 chapters damaged into 3 elements. the 1st half, Big info Technologies, contains introductions to important facts innovations and strategies; massive information analytics; and visualization and studying recommendations. the second one half, LexisNexis chance method to huge Data, specializes in particular applied sciences and methods constructed at LexisNexis to resolve serious difficulties that use great facts analytics. It covers the open resource excessive functionality Computing Cluster (HPCC Systems®) platform and its structure, in addition to parallel info languages ECL and KEL, constructed to successfully clear up huge info difficulties. The 3rd half, Big facts Applications, describes numerous facts in depth functions solved on HPCC platforms. It comprises purposes corresponding to cyber defense, social community analytics together with fraud, Ebola unfold modeling utilizing colossal facts analytics, unsupervised studying, and snapshot classification.
The ebook is meant for a wide selection of individuals together with researchers, scientists, programmers, engineers, designers, builders, educators, and scholars. This booklet is also necessary for enterprise managers, marketers, and traders.
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Extra info for Big Data Technologies and Applications
Another study  attempted to apply the ant-based algorithm to grid computing platform. Since the proposed mining algorithm is extended by the ant clustering algorithm of Deneubourg et al. ,6 Ku-Mahamud modiﬁed the ant behavior of this ant clustering algorithm for big data clustering. That is, each ant will be randomly placed on the grid. This means that the ant clustering algorithm then can be used on a parallel computing environment. The trends of machine learning studies for big data analytics can be divided into twofold: one attempts to make machine learning algorithms run on parallel platforms, such as Radoop , Mahout , and PIMRU ; the other is to redesign the machine learning algorithms to make them suitable for parallel computing or to parallel computing environment, such as neural network algorithms for GPU  and ant-based algorithm for grid .
3, with these operators at hand we will be able to build a complete data analytics system to gather data ﬁrst and then ﬁnd information from the data and display the knowledge to the user. According to our observation, the number of research articles and technical reports that focus on data mining is typically more than the number focusing on other operators, but it does not mean that the other operators of KDD are unimportant. The other operators also play the vital roles in KDD process because they will strongly impact the ﬁnal result of KDD.
Data Analysis Since the data analysis (as shown in Fig. , information or knowledge). The data mining methods  are not limited to data problem speciﬁc methods. , statistical or machine learning technologies) have also been used to analyze the data for many years. In the early stages of data analysis, the statistical methods were used for analyzing the data to help us understand the situation we are facing, such as public opinion poll or TV programme rating. Like the statistical analysis, the problem speciﬁc methods for data mining also attempted to understand the meaning from the collected data.
Big Data Technologies and Applications by Borko Furht, Flavio Villanustre