Oracle Business Intelligence with Machine Learning , 1st ed. Artificial Intelligence Techniques in OBIEE for Actionable BI

Chapter 1:  Overview of Machine Learning

Chapter Goal: Introduce Machine Learning concepts and explain the evolution of machine learning u the realm of artificial intelligence.

No of pages: 50-60

Sub -Topics

1. Overview of Machine Learning

2. Evolution of Artificial Intelligence and Machine Learning 

3. Introduction of Machine Learning and components in OBIEE

Chapter 2:  Business Intelligence, Big Data, and Cloud Computing 

Chapter Goal: Introduce emerging scalable computing platforms and how machine learning can be fully utilized in cloud computing architectures. Introduction to the Oracle Cloud as it pertains to machine learning

No of pages: 50-60

Sub - Topics

1. Overview of Business Intelligence and Big Data

2. Overview of emerging cloud computing architectures and their components  

3. Oracle Cloud architectures, virtualization and leveraging hardware capabilities.

4. Scaling specific machine learning algorithms on the cloud.

Chapter 3: The Oracle R Technologies and R Enterprise

Chapter Goal: Provide an overview of R technologies for the enterprise. It will explain how to perform Big Data advanced analytics with machine learning with the R ecosystem. This chapter will also discuss the expansive set of open source R packages in solutions as well as leveraging R scripts. Also briefly explain how Oracle R Enterprise can be used with OBIEE.

 No of pages : 80-90

Chapter 4:  Why Machine Learning with OBIEE Now

Chapter Goal: Provide the practical reasons and explanation of why machine learning utilizing OBIEE can now be successfully utilized. Include an overview of providing businesses with a competitive advantage using predictive analytics.

No of pages : 70-80

Sub - Topics:  

1. The marriage of artificial intelligence and business intelligence 

2. Evolution of OBIEE to its current version

3. The birth and history of machine learning for OBIEE

4. OBIEE on the Oracle Cloud as an optimal platform

5. Oracle Business Intelligence Cloud Service (BICS)

6. The future of artificial intelligence, in particular machine learning, as it pertain to business intelligence in the corporate enterprise

Chapter 5: Use Case: Machine Learning in OBIEE 12c

Chapter Goal: Provide a real world use case for leveraging machine learning in OBIEE to build an advanced decision-making solution.

No of pages: 50-60

Chapter 6: Implementing Machine Learning in OBIEE 12c

Chapter Goal: Provide a step-by-step guide for implementing machine learning utilizing OBIEE for an advanced decision-making solution 

No of pages: 80-90

Rosendo Abellera : 

From his beginnings in the US intelligence community more than 25 years ago, Rosendo Abellera has made a life-long career out of utilizing data and information as a critical asset. Coupled with his extensive experience in software, he has become an expert practitioner in the field of business intelligence (BI) and analytics and one of the most experienced practitioners in the industry. As a pioneer in BI, he architected analytical solutions and consulted to some prominent, recognized organizations through the years. Spanning across a multitude of industries, this list includes AAA, Accenture, Comcast, US Department of Interior (DOI), ESPN, Harvard University, John Hancock Financial, Koch Industries, Mercury Systems, Pfizer, Staples, and State Street Corp., for example. Moreover, he has held key management positions to establish the BI practices of several prominent consulting firms and also founded an Oracle Partner firm specializing in what was then the newly-branded OBIEE. He is currently a consultant in the Analytics Practice of Mythics Consulting, a leading Oracle Platinum Partner. 

Rosendo is a veteran of the US Air Force and the National Security Agency (NSA) where he served in Europe and Asia as a cryptologist and linguist for several languages.

Lakshman Bulusu is a Senior Oracle Consultant with 23 years of experience in the fields of Oracle RDBMS, SQL, PL/SQL, EDW/BI/EPM, and Oracle-related Java. As an Enterprise-level data warehouse, and business intelligence solution architect/technical manager in the ORACLE RDBMS space, he focused on a best-fit solution architecture and implementation of the Oracle Industry Data Model for telecom. He has worked for major clients in the pharma/healthcare, telecom, financial (banking), retail, and media industry verticals, with special emphasis on cross-platform heterogeneous information architecture and design.