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Recommended Textbook on Intelligent Manufacturing
Authors: Xuanhua Shi, Qiangsheng Hua, Shuying Wang
Book Number: 9787302627142
Publication Date: April 2023
Author Profiles
Xuanhua Shi
Professor and Ph.D. supervisor at Huazhong University of Science and Technology (HUST), Deputy Dean of the School of Computer Science and Technology, Deputy Director of the National-Local Joint Engineering Research Center for Big Data Technology and Systems, Member of the Big Data Expert Committee of China Computer Federation, and Executive Committee Member of High-Performance Computing Professional Committee. He is mainly engaged in research in the fields of cloud computing, big data processing, heterogeneous computing, etc. He has published more than 40 papers in conferences and journals such as ASPLOS, VLDB, ICS, SoCC, TOCS, and his achievements have been recommended as outstanding work by Computer magazine and nominated for the best paper at IEEE Cluster. He has received awards such as the first prize of the CCF Natural Science Award and the first prize of the Ministry of Education's Technological Invention Award.
Qiangsheng Hua
Researcher and Ph.D. supervisor at Huazhong University of Science and Technology (HUST). He graduated from the University of Hong Kong with a PhD in 2009, and is a member of IEEE/ACM/CCF. His main research interests are parallel and distributed computing theory, algorithms and applications. He has published more than 70 papers in important conferences and journals such as INFOCOM, MOBIHOC, ICDCS, SPAA, ATC, ICPP, TCS, ToN, TPDS, and Chinese Science, Journal of Computing, etc. He has also published a monograph "Theory and Algorithms of Distributed Computing" and undertaken multiple projects including the National Natural Science Foundation and sub-projects of the National Key Research and Development Program of China.
Shuying Wang
Doctor of Engineering in Computer Science and Technology, researcher and master's supervisor at the School of Computer and Artificial Intelligence, Southwest Jiaotong University (SWJTU), and core member of "Cloud Service Platform Technology Innovation Team", a key innovation team of the Ministry of Science and Technology. She has long been engaged in research on big data analysis, intelligent manufacturing, and cloud service platform-related topics. She has led and conducted the National High Technology Research and Development Program (863) and National Science and Technology Support Program Projects, more than 20 major science and technology special projects and science and technology support programs in Sichuan Province, as well as more than 20 enterprise cooperation projects. So far, she has published more than 40 papers in the fields of intelligent manufacturing, cloud computing, and big data applications. She has received the second prize of the National Science and Technology Progress Award in 2012, the first prize of the Sichuan Province Science and Technology Progress Award in 2011 and 2006, and the third prize of the Sichuan Province Science and Technology Progress Award in 2003.
Introduction
The manufacturing industry generates a vast amount of data through machine equipment, industrial informatization, and various links in the industrial chain. As the manufacturing industry undergoes digital transformation, there is a growing need for theoretical support and technological updates in managing this data. This book addresses this need by exploring the source and management of manufacturing data, introducing theoretical data models, and classic data management techniques. It also covers high-availability data management and trusted data management techniques, with a focus on meeting the intelligent manufacturing needs of enterprises. The book is organized into five chapters: Introduction, Data Model, Data Management Technology, Highly Available Data Management Technology, and Trusted Data Management. It serves as a valuable teaching reference for database courses in higher education computer programs and is also suitable for researchers, developers, and operations personnel in related fields.
Table of Contents
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Chapter 1 Introduction
1.1Source of Manufacturing Data
1.1.1 Data Collected by Automated Equipment
1.1.2 Data Obtained from Production Equipment
1.1.3 Data from Production Management Systems
1.2 Classification of Manufacturing Data
1.3 Introduction to Manufacturing Data Management Systems
1.3.1 Product Data Management Systems
1.3.2 Enterprise Resource Planning Systems
1.3.3 Manufacturing Execution Systems
References
Chapter 2 Data Models
2.1 Hierarchical Model
2.2 Network Model
2.3 Relational Model
2.3.1 Data Structure of Relational Model
2.3.2 Relational Operations in the Relational Model
2.3.3 Integrity Constraints of the Relational Model
2.4 Graph Model
2.5 Key-Value Model
2.6 Summary
References
Chapter 3 Data Management Techniques
3.1 Overview
3.2 Data Representation and Storage Techniques
3.2.1 Relational Databases
3.2.2 Key-Value Databases
3.2.3 Column-Family Databases
3.2.4 Document Databases
3.2.5 Graph Databases
3.3 Indexing Techniques
3.3.1 B+ Tree Index
3.3.2 Hash Index
3.4 Query Languages
3.4.1 SQL
3.4.2 Gremlin
3.5 Database Design and Application Techniques
3.5.1 Overview of Database Design
3.5.2 Logical Database Architecture Techniques
3.5.3 Physical Database Architecture Techniques
References
Chapter 4 High-Availability Data Management Techniques
4.1 Database High Availability Requirements for Intelligent Manufacturing
4.2 Foundation of Multi-Replica Data Management Techniques
4.2.1 Database-Level Multi-Replica Techniques
4.2.2 Operating System-Level Multi-Replica Techniques
4.3 Enterprise-Level High-Availability Architecture Based on Multi-Replica
4.3.1 Database Dual-Machine Hot Standby Architecture
4.3.2 Data Read-Write Separation Technology Architecture for High-Concurrency Reads
4.3.3 Data Lifecycle Monitoring and Write Database Optimization Technology Architecture
4.4 Enterprise Data Management System Based on Cloud Databases
4.4.1 Cloud Database Technology
4.4.2 Cloud Database Models in Enterprise Data Management
References
Chapter 5 Trusted Data Management
5.1 Intelligent Manufacturing and Trusted Data Management
5.1.1 Strategic Significance of Intelligent Manufacturing
5.1.2 Factors Influencing the Development from Traditional Manufacturing to Intelligent Manufacturing
5.1.3 Why Intelligent Manufacturing Needs Trusted Data Management
5.2 Trusted Data Management Techniques
5.2.1 Achieving Trusted Data Management - Blockchain
5.2.2 Ensuring Data Source - Cryptography
5.2.3 Privacy-Preserving Data Sharing - Privacy Computing
5.3 Applications of Trusted Data Management Techniques in Intelligent Manufacturing
5.3.1 Efficient Supply Chain Management System Based on Blockchain Technology
5.3.2 Data Security Protection through Privacy Computing Technology
5.3.3 Case Study - JD Zhizhen Chain
References
