Modern Big Data Architectures

Provides an up-to-date analysis of big data and multi-agent systems

The term Big Data refers to the cases, where data sets are too large or too complex for traditional data-processing software. With the spread of new concepts such as Edge Computing or the Internet of Things, production, processing and consumption of this data becomes more and more distributed. As a result, applications increasingly require multiple agents that can work together. A multi-agent system (MAS) is a self-organized computer system that comprises multiple intelligent agents interacting to solve problems that are beyond the capacities of individual agents. Modern Big Data Architectures examines modern concepts and architecture for Big Data processing and analytics.

This unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets. Each chapter contains practical examples and detailed solutions suitable for a wide variety of applications. The author, an internationally-recognized expert in Big Data and distributed Artificial Intelligence, demonstrates how base concepts such as agent, actor, and micro-service have reached a point of convergence—enabling next generation systems to be built by incorporating the best aspects of the field. This book:

• Illustrates how data sets are produced and how they can be utilized in various areas of industry and science
• Explains how to apply common computational models and state-of-the-art architectures to process Big Data tasks
• Discusses current and emerging Big Data applications of Artificial Intelligence

Modern Big Data Architectures: A Multi-Agent Systems Perspective is a timely and important resource for data science professionals and students involved in Big Data analytics, and machine and artificial learning.

List of Figures ix

List of Tables xi

Preface xiii

Acknowledgments xv

Acronyms xvii

Chapter 1 Introduction 1

1.1 Motivation 1

1.2 Assumptions 3

1.3 For Whom is This Book? 4

1.4 Book Structure 4

Chapter 2 Evolution of IT Architectures and Paradigms 7

2.1 Evolution of IT Architectures 7

2.1.1 Monolith 7

2.1.2 Service Oriented Architecture 9

2.1.3 Microservices 12

2.2 Actors and Agents 15

2.2.1 Actors 15

2.2.2 Agents 17

2.3 From ACID to BASE, CAP, and NoSQL – The Database (R)evolution 22

2.4 The Cloud 24

2.5 From Distributed Sensor Networks to the Internet of Things and Cyber-Physical Systems 27

2.6 The Rise of Big Data 28

Chapter 3 Sources of Data 31

3.1 The Internet 32

3.1.1 The Semantic Web 32

3.1.2 Linked Data 35

3.1.3 Knowledge Graphs 36

3.1.4 Social Media 38

3.1.5 Web Mining 38

3.2 Scientific Data 40

3.2.1 Biomedical Data 40

3.2.2 Physics and Astrophysics Data 41

3.2.3 Environmental Sciences 44

3.3 Industrial Data 45

3.3.1 Smart Factories 45

3.3.2 SmartGrid 47

3.3.3 Aviation 47

3.4 Internet of Things 48

Chapter 4 Big Data Tasks 51

4.1 Recommender Systems 51

4.2 Search 52

4.3 Ad-tech and RTB Algorithms 55

4.4 Cross-Device Graph Generation 57

4.5 Forecasting and Prediction Systems 58

4.6 Social Media Big Data 59

4.7 Anomaly and Fraud Detection 61

4.8 New Drug Discovery 63

4.9 Smart Grid Control and Monitoring 64

4.10 IoT and Big Data Applications 65

Chapter 5 Cloud Computing 67

5.1 Cloud Enabled Architectures 67

5.1.1 Cloud Management Platforms 67

5.1.2 Efficient Cloud Computing 73

5.1.3 Distributed Storage Systems 75

5.2 Agents and the Cloud 82

5.2.1 Multi-agent Versus Cloud Paradigms 83

5.2.2 Agents in the Cloud 83

Chapter 6 Big Data Architectures 87

6.1 Big Data Computation Models 87

6.1.1 MapReduce 87

6.1.2 Directed Acyclic Graph Models 89

6.1.3 All-Pairs 92

6.1.4 Very Large Bitmap Operations 93

6.1.5 Message Passing Interface 94

6.1.6 Graphical Processing Unit Computing 95

6.2 Publish-Subscribe Systems 97

6.3 Stream Processing 99

6.3.1 Information Flow Processing Concepts 99

6.3.2 Stream Processing Systems 101

6.4 Higer Level Big Data Architectures 110

6.4.1 Spark 110

6.4.2 Lambda 112

6.4.3 Multi-Agent View of the Lambda Architecture 113

6.4.4 Questioning the Lambda 115

6.5 Industry and Other Approaches 116

6.6 Actor and Agent-Based Big Data Architectures 118

Chapter 7 Big Data Analytics, Mining, and Machine Learning 121

7.1 To SQL or Not to SQL 122

7.1.1 SQL Hadoop Interfaces 123

7.1.2 From Shark to SparkSQL 125

7.2 Big Data Mining and Machine Learning 128

7.2.1 Graph Mining 133

7.2.2 Agent Based Machine Learning and Data Mining 134

Chapter 8 Physically Distributed Systems – Mobile Cloud, Internet of Things, Edge Computing 137

8.1 Mobile Cloud 138

8.2 Edge and Fog Computing 145

8.2.1 Business Case: Mobile Context Aware Recommender System 147

8.3 Internet of Things 148

8.3.1 IoT Fundamentals 148

8.3.2 IoT and the Cloud 151

8.3.3 MAS in IoT 156

Chapter 9 Summary 159

Bibliography 161

Index 179

DOMINIK RYŻKO is an Assistant Professor at the Institute of Computer Science at Warsaw University of Technology. His research interests include Big Data and Distributed Artificial Intelligence. He is widely published, serves on program committees at international conferences, and is Vice President of artificial intelligence and analytics at Adform, a global ad-tech platform provider. He also spent three years at Allegro Group as the Chief Data Scientist where he oversaw Data Science activities, design and methodology of experiments, and model building.

The current generation of data systems relies on two concepts that have only recently risen to prominence: Big Data and Artificial Intelligence. Together, Big Data and AI make possible the complex computational tasks that are essential to every field, industry, and corner of contemporary life. As we continue to demand additional value and greater problem-solving ability from our systems, we must find additional ways of conceiving of data processing. New insights are needed to launch a new generation of processing and analytics.

In Modern Big Data Architectures, data scientist Dominik Ryżko presents a view of Big Data architectures that has the potential to expand our thinking on intelligent systems. In the context of a comprehensive overview of Big Data applications covering everything from social media and IoT to fog computing and higher-level industrial processing, Ryżko traces the concept of multi-agent systems (MAS) as a potential paradigm for distributed intelligent systems. This unique book is the first to provide practical guidance on Big Data processing alongside an analysis of MAS and its potential to add value.

Although MAS has not yet risen to prominence in the general public, it has already begun to influence how systems architects and data analysts solve problems using Big Data. A MAS consists of multiple "agents"—autonomous intelligent entities capable of learning and completing specific tasks. When several such agents, each designed with its own specialty, collaborate to solve complex problems, the resulting MAS is capable of exhilarating feats. Big Data and machine learning specialists are already using similar technologies. With this book, readers can fully integrate the power of MAS in their Big Data applications, for more powerful, adaptive, and intelligent systems.

Implement adaptive systems for Big Data processing using next-generation concepts

As artificial intelligence technology advances, so does our ability to solve complex problems by analyzing Big Data. Modern Big Data Architectures offers a look into the future of Big Data, underscoring the untapped potential of the multi-agent systems (MAS) view.

Author Dominik Ryżko, an international expert on processing massive datasets using distributed AI, shows how multiple autonomous AI "agents" can work collaboratively to solve Big Data problems too complex, too difficult, or too large for a typical monolithic system. Considering how the MAS paradigm can improve our current approaches to Big Data, this book covers:

• Agent-based (and non-agent-based) methods of producing Big Data sets
• Key Big Data applications, from product recommendations to drug discovery
• Traditional and next-generation Big Data architectures
• Data mining, machine learning, and SQL analytics using the MAS approach
• Big Data and agents in cloud computing, fog computing, and Internet of Things

Researchers and practitioners alike in the fields of Big Data, analytics, machine learning, cloud computing, and distributed AI will discover new ideas in Modern Big Data Architectures.