Utilizing Big Data Paradigms For Business Intelligence
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Utilizing Big Data Paradigms for Business Intelligence
Because efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations, data analysis is an important part of modern business administration. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Utilizing Big Data Paradigms for Business Intelligence is a pivotal reference source that provides vital research on how to address the challenges of data extraction in business intelligence using the five “Vs” of big data: velocity, volume, value, variety, and veracity. This book is ideally designed for business analysts, investors, corporate managers, entrepreneurs, and researchers in the fields of computer science, data science, and business intelligence.
Seven Methods for Transforming Corporate Data Into Business Intelligence
Information systems: past, present, and emerging; Intelligence density a metric for knowledge work; The vocabulary of intelligence density; Method one: data-driven decision support; Method two: evolving solutions: genetic algorithms; Method three: simulating the brain to solve problems: neural networks; Method four: putting expert resoning in a box: rule-based systems; Method five: dealing with linguistic ambiguity: fuzzy logic; Method six: soilving problems by analogy case-based resoning; Method seven: deriving rules from data: machine learning; Appendix saving time and money with object; Appendix case studies.
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
Author: Aboul Ella Hassanien
language: en
Publisher: Springer Nature
Release Date: 2020-12-14
This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.