Oracle Business Intelligence with Machine Learning Artificial Intelligence Techniques in OBIEE for Actionable BI /

Use machine learning and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. This book follows a when-to, why-to, and how-to approach to explain the key steps involved in utilizing the artificial intelligence components now available for a successful OBIEE impleme...

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Bibliographic Details
Main Authors: Abellera, Rosendo (Author, http://id.loc.gov/vocabulary/relators/aut), Bulusu, Lakshman (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:English
Published: Berkeley, CA : Apress : Imprint: Apress, 2018.
Edition:1st ed. 2018.
Subjects:
Online Access:Full Text via HEAL-Link
Description
Summary:Use machine learning and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. This book follows a when-to, why-to, and how-to approach to explain the key steps involved in utilizing the artificial intelligence components now available for a successful OBIEE implementation. Oracle Business Intelligence with Machine Learning covers various technologies including using Oracle OBIEE, R Enterprise, Spatial Maps, and machine learning for advanced visualization and analytics. The machine learning material focuses on learning representations of input data suitable for a given prediction problem. This book focuses on the practical aspects of implementing machine learning solutions using the rich Oracle BI ecosystem. The primary objective of this book is to bridge the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to machine learning with OBIEE. You will: See machine learning in OBIEE Master the fundamentals of machine learning and how it pertains to BI and advanced analytics Gain an introduction to Oracle R Enterprise Discover the practical considerations of implementing machine learning with OBIEE.
Physical Description:XIV, 199 p. online resource.
ISBN:9781484232552
DOI:10.1007/978-1-4842-3255-2