Big Data Analytics in Smart Manufacturing
Principles and Practices
Seiten
2022
Chapman & Hall/CRC (Verlag)
978-1-032-06551-9 (ISBN)
Chapman & Hall/CRC (Verlag)
978-1-032-06551-9 (ISBN)
The ultra-secure and immutable ledgers, strong, consensus mechanisms, decentralization, and self-sovereign identity of AI and IoT technologies have tremendous potential to rebalance and improve machine learning algorithms. This book discusses the possibility of using AI, IoT and machine learning for the enhancement of healthcare systems.
The significant objective of this edited book is to bridge the gap between smart manufacturing and big data by exploring the challenges and limitations. Companies employ big data technology in the manufacturing field to acquire data about the products. Manufacturing companies could gain a deep business insight by tracking customer details, monitoring fuel consumption, detecting product defects, and supply chain management. Moreover, the convergence of smart manufacturing and big data analytics currently suffers due to data privacy concern, short of qualified personnel, inadequate investment, long-term storage management of high-quality data. The technological advancement makes the data storage more accessible, cheaper and the convergence of these technologies seems to be more promising in the recent era. This book identified the innovative challenges in the industrial domains by integrating heterogeneous data sources such as structured data, semi-structures data, geo-spatial data, textual information, multimedia data, social networking data, etc. It promotes data-driven business modelling processes by adopting big data technologies in the manufacturing industry. Big data analytics is emerging as a promising discipline in the manufacturing industry to build the rigid industrial data platforms. Moreover, big data facilitates process automation in the complete lifecycle of product design and tracking. This book is an essential guide and reference since it synthesizes interdisciplinary theoretical concepts, definitions, and models, involved in smart manufacturing domain. It also provides real-world scenarios and applications, making it accessible to a wider interdisciplinary audience.
Features
The readers will get an overview about the smart manufacturing system which enables optimized manufacturing processes and benefits the users by increasing overall profit
The researchers will get insight about how the big data technology leverages in finding new associations, factors and patterns through data stream observations in real time smart manufacturing systems
The industrialist can get an overview about the detection of defects in design, rapid response to market, innovative products to meet the customer requirement which can benefit their per capita income in better way
Discusses technical viewpoints, concepts, theories, and underlying assumptions that are used in smart manufacturing
Information delivered in a user-friendly manner for students, researchers, industrial experts, and business innovators, as well as for professionals and practitioners
The significant objective of this edited book is to bridge the gap between smart manufacturing and big data by exploring the challenges and limitations. Companies employ big data technology in the manufacturing field to acquire data about the products. Manufacturing companies could gain a deep business insight by tracking customer details, monitoring fuel consumption, detecting product defects, and supply chain management. Moreover, the convergence of smart manufacturing and big data analytics currently suffers due to data privacy concern, short of qualified personnel, inadequate investment, long-term storage management of high-quality data. The technological advancement makes the data storage more accessible, cheaper and the convergence of these technologies seems to be more promising in the recent era. This book identified the innovative challenges in the industrial domains by integrating heterogeneous data sources such as structured data, semi-structures data, geo-spatial data, textual information, multimedia data, social networking data, etc. It promotes data-driven business modelling processes by adopting big data technologies in the manufacturing industry. Big data analytics is emerging as a promising discipline in the manufacturing industry to build the rigid industrial data platforms. Moreover, big data facilitates process automation in the complete lifecycle of product design and tracking. This book is an essential guide and reference since it synthesizes interdisciplinary theoretical concepts, definitions, and models, involved in smart manufacturing domain. It also provides real-world scenarios and applications, making it accessible to a wider interdisciplinary audience.
Features
The readers will get an overview about the smart manufacturing system which enables optimized manufacturing processes and benefits the users by increasing overall profit
The researchers will get insight about how the big data technology leverages in finding new associations, factors and patterns through data stream observations in real time smart manufacturing systems
The industrialist can get an overview about the detection of defects in design, rapid response to market, innovative products to meet the customer requirement which can benefit their per capita income in better way
Discusses technical viewpoints, concepts, theories, and underlying assumptions that are used in smart manufacturing
Information delivered in a user-friendly manner for students, researchers, industrial experts, and business innovators, as well as for professionals and practitioners
P Suresh; Meenaskshi Sharma
1. Machine Learning Techniques and Big Data Analytics for Smart Manufacturing. 2.Data-Driven Paradigm for Smart Manufacturing In The Context of Big Data Analytics. 3.Data-Driven Models in Machine Learning- An Enabler of Smart Manufacturing. 4. Local Time Invariant Learning from Industrial Big Data for Predictive Maintenance in Smart Manufacturing. 5.Integration of Industrial IoT and Big data Analytics for Smart Manufacturing Industries: Perspectives and Challenges. 6.Multimodal Architecture for Emotion Prediction in Videos using Ensemble Learning. 7. Deep PHM: IOT based Deep Learning approach on Prediction of Prognostics and Health Management of an Aircraft Engine. 8.A Comprehensive Study on Accelerating Smart Manufacturers using Ubiquitous Robotic Technology. 9.Machine Learning Techniques and Big Data Tools in Design and Manufacturing. 10.Principles of Comprehension of IoT and Smart Manufacturing System.
| Erscheinungsdatum | 28.11.2022 |
|---|---|
| Zusatzinfo | 25 Tables, black and white; 51 Line drawings, black and white; 17 Halftones, black and white; 68 Illustrations, black and white |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 535 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 1-032-06551-6 / 1032065516 |
| ISBN-13 | 978-1-032-06551-9 / 9781032065519 |
| Zustand | Neuware |
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