By Borko Furht, Flavio Villanustre
The goal of this booklet is to introduce the fundamental recommendations of huge facts computing after which to explain the full resolution of massive information difficulties utilizing HPCC, an open-source computing platform.
The e-book contains 15 chapters damaged into 3 components. the 1st half, Big information Technologies, comprises introductions to important facts suggestions and methods; monstrous info analytics; and visualization and studying suggestions. the second one half, LexisNexis threat method to massive Data, specializes in particular applied sciences and strategies built at LexisNexis to unravel severe difficulties that use sizeable 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 remedy massive info difficulties. The 3rd half, Big info Applications, describes a variety of information in depth purposes solved on HPCC structures. It comprises functions resembling cyber safety, social community analytics together with fraud, Ebola unfold modeling utilizing vast info analytics, unsupervised studying, and photo 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 important for company managers, marketers, and traders.
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Extra info for Big Data Technologies and Applications
For example, genetic algorithm, one of the machine learning algorithms, can not only be used to solve the clustering problem , it can also be used to solve the frequent pattern mining problem . The potential of machine learning is not merely for solving different mining problems in data analysis operator of KDD; it also has the potential of enhancing the performance of the other parts of KDD, such as feature reduction for the input operators . A recent study  shows that some traditional mining algorithms, statistical methods, preprocessing solutions, and even the GUI’s have been applied to several representative tools and platforms for big data analytics.
Moreover, although several data analytics and frameworks have been presented in recent years, with their pros and cons being discussed in different studies, a complete discussion from the perspective of data mining and knowledge discovery in databases still is needed. As a result, this paper is aimed at providing a brief review for the researchers on the data mining and distributed computing domains to have a basic idea to use or develop data analytics for big data. 2 shows the roadmap of this paper, and the remainder of the paper is organized as follows.
1 Confusion matrix of a classiﬁer  Actual positive Actual negative Classiﬁed positive Classiﬁed negative TP FP FN TN Output the Result 21 results, of course, how fast they can get the ﬁnal mining results will become the most important research topic. , classiﬁcation rules) is found by data mining methods, the two essential research topics are: (1) the work to navigate and explore the meaning of the results from the data analysis to further support the user to do the applicable decision can be regarded as the interpretation operator , which in most cases, gives useful interface to display the information  and (2) a meaningful summarization of the mining results  can be made to make it easier for the user to understand the information from the data analysis.
Big Data Technologies and Applications by Borko Furht, Flavio Villanustre