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.

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• List of Figures ixList of Tables xiPreface xiiiAcknowledgments xvAcronyms xviiChapter 1 Introduction 11.1 Motivation 11.2 Assumptions 31.3 For Whom is This Book? 41.4 Book Structure 4Chapter 2 Evolution of IT Architectures and Paradigms 72.1 Evolution of IT Architectures 72.1.1 Monolith 72.1.2 Service Oriented Architecture 92.1.3 Microservices 122.2 Actors and Agents 152.2.1 Actors 152.2.2 Agents 172.3 From ACID to BASE, CAP, and NoSQL – The Database (R)evolution 222.4 The Cloud 242.5 From Distributed Sensor Networks to the Internet of Things and Cyber-Physical Systems 272.6 The Rise of Big Data 28Chapter 3 Sources of Data 313.1 The Internet 323.1.1 The Semantic Web 323.1.2 Linked Data 353.1.3 Knowledge Graphs 363.1.4 Social Media 383.1.5 Web Mining 383.2 Scientific Data 403.2.1 Biomedical Data 403.2.2 Physics and Astrophysics Data 413.2.3 Environmental Sciences 443.3 Industrial Data 453.3.1 Smart Factories 453.3.2 SmartGrid 473.3.3 Aviation 473.4 Internet of Things 48Chapter 4 Big Data Tasks 514.1 Recommender Systems 514.2 Search 524.3 Ad-tech and RTB Algorithms 554.4 Cross-Device Graph Generation 574.5 Forecasting and Prediction Systems 584.6 Social Media Big Data 594.7 Anomaly and Fraud Detection 614.8 New Drug Discovery 634.9 Smart Grid Control and Monitoring 644.10 IoT and Big Data Applications 65Chapter 5 Cloud Computing 675.1 Cloud Enabled Architectures 675.1.1 Cloud Management Platforms 675.1.2 Efficient Cloud Computing 735.1.3 Distributed Storage Systems 755.2 Agents and the Cloud 825.2.1 Multi-agent Versus Cloud Paradigms 835.2.2 Agents in the Cloud 83Chapter 6 Big Data Architectures 876.1 Big Data Computation Models 876.1.1 MapReduce 876.1.2 Directed Acyclic Graph Models 896.1.3 All-Pairs 926.1.4 Very Large Bitmap Operations 936.1.5 Message Passing Interface 946.1.6 Graphical Processing Unit Computing 956.2 Publish-Subscribe Systems 976.3 Stream Processing 996.3.1 Information Flow Processing Concepts 996.3.2 Stream Processing Systems 1016.4 Higer Level Big Data Architectures 1106.4.1 Spark 1106.4.2 Lambda 1126.4.3 Multi-Agent View of the Lambda Architecture 1136.4.4 Questioning the Lambda 1156.5 Industry and Other Approaches 1166.6 Actor and Agent-Based Big Data Architectures 118Chapter 7 Big Data Analytics, Mining, and Machine Learning 1217.1 To SQL or Not to SQL 1227.1.1 SQL Hadoop Interfaces 1237.1.2 From Shark to SparkSQL 1257.2 Big Data Mining and Machine Learning 1287.2.1 Graph Mining 1337.2.2 Agent Based Machine Learning and Data Mining 134Chapter 8 Physically Distributed Systems – Mobile Cloud, Internet of Things, Edge Computing 1378.1 Mobile Cloud 1388.2 Edge and Fog Computing 1458.2.1 Business Case: Mobile Context Aware Recommender System 1478.3 Internet of Things 1488.3.1 IoT Fundamentals 1488.3.2 IoT and the Cloud 1518.3.3 MAS in IoT 156Chapter 9 Summary 159Bibliography 161Index 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.

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• List of Figures ixList of Tables xiPreface xiiiAcknowledgments xvAcronyms xviiChapter 1 Introduction 11.1 Motivation 11.2 Assumptions 31.3 For Whom is This Book? 41.4 Book Structure 4Chapter 2 Evolution of IT Architectures and Paradigms 72.1 Evolution of IT Architectures 72.1.1 Monolith 72.1.2 Service Oriented Architecture 92.1.3 Microservices 122.2 Actors and Agents 152.2.1 Actors 152.2.2 Agents 172.3 From ACID to BASE, CAP, and NoSQL – The Database (R)evolution 222.4 The Cloud 242.5 From Distributed Sensor Networks to the Internet of Things and Cyber-Physical Systems 272.6 The Rise of Big Data 28Chapter 3 Sources of Data 313.1 The Internet 323.1.1 The Semantic Web 323.1.2 Linked Data 353.1.3 Knowledge Graphs 363.1.4 Social Media 383.1.5 Web Mining 383.2 Scientific Data 403.2.1 Biomedical Data 403.2.2 Physics and Astrophysics Data 413.2.3 Environmental Sciences 443.3 Industrial Data 453.3.1 Smart Factories 453.3.2 SmartGrid 473.3.3 Aviation 473.4 Internet of Things 48Chapter 4 Big Data Tasks 514.1 Recommender Systems 514.2 Search 524.3 Ad-tech and RTB Algorithms 554.4 Cross-Device Graph Generation 574.5 Forecasting and Prediction Systems 584.6 Social Media Big Data 594.7 Anomaly and Fraud Detection 614.8 New Drug Discovery 634.9 Smart Grid Control and Monitoring 644.10 IoT and Big Data Applications 65Chapter 5 Cloud Computing 675.1 Cloud Enabled Architectures 675.1.1 Cloud Management Platforms 675.1.2 Efficient Cloud Computing 735.1.3 Distributed Storage Systems 755.2 Agents and the Cloud 825.2.1 Multi-agent Versus Cloud Paradigms 835.2.2 Agents in the Cloud 83Chapter 6 Big Data Architectures 876.1 Big Data Computation Models 876.1.1 MapReduce 876.1.2 Directed Acyclic Graph Models 896.1.3 All-Pairs 926.1.4 Very Large Bitmap Operations 936.1.5 Message Passing Interface 946.1.6 Graphical Processing Unit Computing 956.2 Publish-Subscribe Systems 976.3 Stream Processing 996.3.1 Information Flow Processing Concepts 996.3.2 Stream Processing Systems 1016.4 Higer Level Big Data Architectures 1106.4.1 Spark 1106.4.2 Lambda 1126.4.3 Multi-Agent View of the Lambda Architecture 1136.4.4 Questioning the Lambda 1156.5 Industry and Other Approaches 1166.6 Actor and Agent-Based Big Data Architectures 118Chapter 7 Big Data Analytics, Mining, and Machine Learning 1217.1 To SQL or Not to SQL 1227.1.1 SQL Hadoop Interfaces 1237.1.2 From Shark to SparkSQL 1257.2 Big Data Mining and Machine Learning 1287.2.1 Graph Mining 1337.2.2 Agent Based Machine Learning and Data Mining 134Chapter 8 Physically Distributed Systems – Mobile Cloud, Internet of Things, Edge Computing 1378.1 Mobile Cloud 1388.2 Edge and Fog Computing 1458.2.1 Business Case: Mobile Context Aware Recommender System 1478.3 Internet of Things 1488.3.1 IoT Fundamentals 1488.3.2 IoT and the Cloud 1518.3.3 MAS in IoT 156Chapter 9 Summary 159Bibliography 161Index 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.

Select Guide Rating

• List of Figures ixList of Tables xiPreface xiiiAcknowledgments xvAcronyms xviiChapter 1 Introduction 11.1 Motivation 11.2 Assumptions 31.3 For Whom is This Book? 41.4 Book Structure 4Chapter 2 Evolution of IT Architectures and Paradigms 72.1 Evolution of IT Architectures 72.1.1 Monolith 72.1.2 Service Oriented Architecture 92.1.3 Microservices 122.2 Actors and Agents 152.2.1 Actors 152.2.2 Agents 172.3 From ACID to BASE, CAP, and NoSQL – The Database (R)evolution 222.4 The Cloud 242.5 From Distributed Sensor Networks to the Internet of Things and Cyber-Physical Systems 272.6 The Rise of Big Data 28Chapter 3 Sources of Data 313.1 The Internet 323.1.1 The Semantic Web 323.1.2 Linked Data 353.1.3 Knowledge Graphs 363.1.4 Social Media 383.1.5 Web Mining 383.2 Scientific Data 403.2.1 Biomedical Data 403.2.2 Physics and Astrophysics Data 413.2.3 Environmental Sciences 443.3 Industrial Data 453.3.1 Smart Factories 453.3.2 SmartGrid 473.3.3 Aviation 473.4 Internet of Things 48Chapter 4 Big Data Tasks 514.1 Recommender Systems 514.2 Search 524.3 Ad-tech and RTB Algorithms 554.4 Cross-Device Graph Generation 574.5 Forecasting and Prediction Systems 584.6 Social Media Big Data 594.7 Anomaly and Fraud Detection 614.8 New Drug Discovery 634.9 Smart Grid Control and Monitoring 644.10 IoT and Big Data Applications 65Chapter 5 Cloud Computing 675.1 Cloud Enabled Architectures 675.1.1 Cloud Management Platforms 675.1.2 Efficient Cloud Computing 735.1.3 Distributed Storage Systems 755.2 Agents and the Cloud 825.2.1 Multi-agent Versus Cloud Paradigms 835.2.2 Agents in the Cloud 83Chapter 6 Big Data Architectures 876.1 Big Data Computation Models 876.1.1 MapReduce 876.1.2 Directed Acyclic Graph Models 896.1.3 All-Pairs 926.1.4 Very Large Bitmap Operations 936.1.5 Message Passing Interface 946.1.6 Graphical Processing Unit Computing 956.2 Publish-Subscribe Systems 976.3 Stream Processing 996.3.1 Information Flow Processing Concepts 996.3.2 Stream Processing Systems 1016.4 Higer Level Big Data Architectures 1106.4.1 Spark 1106.4.2 Lambda 1126.4.3 Multi-Agent View of the Lambda Architecture 1136.4.4 Questioning the Lambda 1156.5 Industry and Other Approaches 1166.6 Actor and Agent-Based Big Data Architectures 118Chapter 7 Big Data Analytics, Mining, and Machine Learning 1217.1 To SQL or Not to SQL 1227.1.1 SQL Hadoop Interfaces 1237.1.2 From Shark to SparkSQL 1257.2 Big Data Mining and Machine Learning 1287.2.1 Graph Mining 1337.2.2 Agent Based Machine Learning and Data Mining 134Chapter 8 Physically Distributed Systems – Mobile Cloud, Internet of Things, Edge Computing 1378.1 Mobile Cloud 1388.2 Edge and Fog Computing 1458.2.1 Business Case: Mobile Context Aware Recommender System 1478.3 Internet of Things 1488.3.1 IoT Fundamentals 1488.3.2 IoT and the Cloud 1518.3.3 MAS in IoT 156Chapter 9 Summary 159Bibliography 161Index 179