Modern Oracle Enterprise Architecture

Discover Oracle's Hidden Gems for Next Generation Database and Application Migrations

JAVID UR RAHAMAN

• Publisher: BPB Publications
• Year: 2021
• Topic: Computer Science
• eBook ISBN:
  9789390684830
• Subtopic: Computer Science General
• Index: Computer Science

A comprehensive innovative product handbook for managers designing and deploying enterprise business solutions.

Key Features
? Covers proven technical approaches in migrating your enterprise systems to Oracle Cloud Computing.
? A handbook for decision-makers on using Oracle Product Suite for digital transformation.
? Understand the Oracle product benefits and leveraging capital investment to avail great measurable ROI and TCO.

Description
The Oracle Enterprise Architecture Framework emerges from the on-site legacy to current cloud native and is called Modern Oracle Enterprise Architecture. It aims to clear the path for critical business application workloads in the field of database and the application architecture to hybrid and cloud applications. This is a very handy book for chief decision-makers and professional cloud solution engineers.As the current cloud computing services are agile and pay-as-you-go (PAYG) based subscription including multi-year cost model thus a more agile approach is covered throughout the book.This book will help readers to achieve their database and application system solution architecture career objectives more quickly without spending years. The readers can prevent committing errors, recovering from them, and learning things the hard way.

What you will learn
? 360-degree view of Oracle database and application products.
? Transition to hybrid cloud identity services via Oracle Identity Cloud platform.
? Understand and implement Oracle accessibility and architecture observability.

Who this book is for
This book is for decision-makers, business architects, system development teams, technological professionals and product teams who want to use the Oracle stack's hidden capabilities to develop, manage and keep enhancing enterprise systems.

Table of Contents
01. Artificial Intelligence for Cloud Computing
02. Business

CHAPTER 1

Artificial Intelligence for Cloud Computing

Introduction

Artificial Intelligence (AI) for Cloud Computing is revolutionizing the untapped opportunities in the digital space. Artificial Intelligence as a Service (AIaaS) is the next decade buzzword and will take over a significant chunk of the platform services.

Structure

In this chapter, we will cover the following topics:

• Artificial Intelligence (AI) autonomous framework
• Anatomy of Artificial Intelligence as a Service (AIaaS)

Objective

After studying this chapter, readers will understand AI concepts in the Oracle technology stack and how Oracle has embedded AI in Oracle Cloud computing.

Artificial Intelligence (AI) An autonomous framework

An autonomous framework is called serverless computing powered by AI and has been built as a service for cloud application development.

The following diagram depicts the AI DNA for cloud computing and gives a 360-degree view of how Cloud Computing powers Artificial Intelligence (AI) with machine learning (ML):

Figure 1.1: DNA of AI Cloud Computing

Definition of AI

• The recreation of human emotions and actions by machine algorithms is defined as follows:
  Artificial intelligence is the creative ability of a computer or a programmed robot controlled by a humanoid to execute the tasks that are usually performed by humans which require human intelligence and discernment; this process is recurring and consists of accumulated intelligence based on repetitiveness, and that attains near-zero perfection for the execution of the complex set of parallel and sequential tasks which are impossible by a human brain due to the limited processing capability.
• Machine Learning (ML) is a technical branch of AI that allows software applications to predict outcomes without explicitly programming. The machine learning algorithms use the historical data with advanced modeling to indicate the near-accurate output values.
• ML is the amalgamation of the computer algorithms that allow the computer programs to improve through the experience automatically — ML is one of the ways to achieve AI. ML relies on small-to-large datasets by examining and comparing the data to find the common patterns and explore the nuances.
• The supervised learning algorithms are the model relationships and dependencies between the target prediction output and the input.
• Predict the output values for the new data based on the entity relationships learned from the previous datasets.
• Unsupervised learning is a type of machine learning in the family of machine learning algorithms, which has the primary uses of pattern detection and descriptive modeling of the data; these algorithms do not have output categories or labels.

Anatomy of Artificial Intelligence as a Service (AIaaS)

Artificial Intelligence-powered Cloud Computing is a stellar combination. Modern digital assistants like Siri, Google Home, and Amazon's Alexa combine AI and Cloud Computing to take the tasks performed by humans to a near-zero tolerance. The consumers can make the purchase decisions, adjust a home thermostat, or play songs over an internet-connected voice playback device with a quick verbal conversation. The contiguous flow of the AI and cloud computing services make the seamless business processes transition with low latency, further improving the response times, resulting in cost savings. The industry has not yet realized the blend of these two technologies, artificial intelligence and cloud computing — making these connected, intuitive, and agile.

The following diagram depicts the AI Computing Funnel for Cloud Computing how the data patterns get through the funnel for 99.99% predicate accuracy using ML:

Figure 1.2: AI Cloud Computing funnel diagram

The AI capabilities promised a medium to large-scale business transition to the cloud computing environments to leverage more insight-driven efficiency. Cloud Computing with the edge services offers the businesses more flexibility, agility, and cost savings by hosting the data and applications in the edge cloud — an intelligent and innovative process to identify the low-latency geotagged for more excellent responsiveness to the businesses during digital adoption. The AI layering with cloud edge computing accelerates the companies to manage their data patterns and insights in the information, deliver superior customer experiences, and optimize the business workflows.

The global value of the AI market will surpass more than an estimated $500 billion annually by 2030; the value will occur as the AI powers cloud edge computing. In turn, Cloud Computing catalysts as a growth engine rapidly increase AI's impact in the larger market.

The research firms recently conducted a study to explore how AI could impact the value. AI has excellent capabilities to leverage for the companies to grow in their respective verticals. A lack of technical talent and vetted use cases with the limited on-premises infrastructure made it less attainable for many organizations.

Through the cloud control plane accessing services, in short, the cloud is democratizing access to AI by giving the companies the ability to leverage current identity services securely seamlessly.

There are the following four types of AI theories:

• Reactive machines
• Limited memory
• Theory of mind
• Self-awareness

Anatomy of AI

The anatomy of Artificial Intelligence is described as follows:

• AIOps functions: Artificial Intelligence for automating the repetitive tasks typically performed by a set of augmented computer programs is called AIOps functions.
• AIML: This is the Artificial Intelligence framework for the machine learning algorithms that have inhibited the memory to the continuous learning mechanism. Using the ML cloud services, the users can apply the machine learning algorithms efficiently and, comparatively, in a shorter period apart from storing and networking. The prominent AI applications in the cloud are the IoT cloud Internet of Things and cloud architecture that power IoT.
• Conversational AI: The conversational AI application is a Natural Language Processing (NLP) of contextual, pre-aligned human conversations, which uses the help of a machine language bot. The next maturity level of conversational AI is Virtual Personal Assistants. Examples of these are Amazon Alexa, Apple's Siri, and Google Home.

The top use cases of Cloud Computing using AI are as follows:

• Data mining: Data mining is a technology pattern that understands the data three-dimensional and extracts the value.
• Agile development: Agile development is a continuous development framework where AI and ML contribute to store all the learning and enhancements of any product or service.
• Transforming IT infrastructure: It transforms legacy applications by undermining the data and systematically storing and sharing the 360-degree views.
• Seamless data access: The platform access ability is based on intelligent data access patterns, which are impossible with a human analogy.
• Analytics and prediction: Using AI and ML, the data analytics combines the Cloud Compute transform data into predictions based on the historical data.
• Cloud security automation: Machine learning reads all the access logs and augments the patterns to determine the security loopholes. It keeps improving on its own with its learning ability.

Artificial Intelligence (AI) for Cloud Computing

The Artificial Intelligence for cloud computing is further classified in the below subsections specific to oracle enterprise stack as Autonomous Framework and Data Management with AI & AI SaaS Amalgamation.

Autonomous Framework (self-managing with AI ML)

AI is embedded into the IT infrastructure to help streamline the business workloads and automate complex, repetitive tasks. AI will enhance the complicated private and public cloud instances and leverage the AI tools to monitor, manage, and even self-heal when an issue occurs. AI can automate the core business workflows; the analytical capabilities can create and enhance the largely independent processes. The routine and mundane business processes can be managed and monitored by the automated system, further helping the large IT teams to improve the cloud computing efficiencies and allowing them to focus on the strategic activities.

Data management with AI

At the cloud services level, the AI tools are the improved data management capabilities. Consider the volume of data that today's businesses generate Cloud Computing services using AI tools to help with the specific aspects of the data process.

In banking, for example, even the most minor financial organization may need to monitor thousands of transactions per day, analyze all the data patterns for protecting the customers' data and give the best user experience with the most accurately predicted suggestions on the platform. The AI tools can help streamline the ingested, updated, and managed data to offer accurate and real-time data to the clients. The exact process can also help the fraudulent flag activity or identify the other areas of risk. Similar improvements can significantly impact areas such as marketing, customer service, and supply-chain data management.

AI–SaaS amalgamation

For the dynamic cloud Services as a Service (SaaS) transforming businesses, AI relies on the tools, competitor sales, and market trends. Advanced analysis is based on modeling and pulling on the deep neural networks using the following two learning paths:

• Supervised learning (classification/regression)
• Unsupervised learning (clustering)

As for AI for the SaaS services, some of them are listed as follows as per the current trend:

• Big Data as Service
• Pred...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Dedication Page
• About the Author
• About the Reviewers
• Acknowledgement
• Preface
• Errata
• Table of Contents
• 1. Artificial Intelligence for Cloud Computing
• 2. Business Benefits of Migrating and Operating on Oracle Cloud
• 3. Move and Optimize The Cost for Oracle E-Business Suite on Cloud Compute
• 4. Contemplating IaaS, PaaS, and SaaS Migration for On-Premises Legacy Systems
• 5. Oracle Autonomous Dedicated for Oracle E-Business Suite Customers
• 6. Business Benefits of Running PeopleSoft on Oracle Autonomous Dedicated or Shared
• 7. Cloud Agile Maximum Availability Architecture (CAMAA)
• 8. Agile Maximum Security Architecture (AMSA)
• 9. Agile Accessibility and Observability Architecture. Agile AOA (AAOA)
• 10. Fleet Management for On-Premises and Cloud (DBaaS and IaaS)Database Stack
• 11. Identity Transition from Identity Manager (IDM) to Universal Directory (OUD) and Identity Cloud Suite
• 12. Decision Analysis Resolution (DAR) For Oracle E-Business Suite on Cloud Compute
• 13. The Hidden Jewel on Oracle's Crown: Oracle Enterprise Manager Site Guard
• 14. Case Study 01
• 15. Case Study 02
• 16. Case Study 03
• Index