Data Quality and Big Data Analytics for Smart Manufacturing
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 4839
Special Issue Editors
Interests: Internet of Things; smart manufacturing; smart city; artificial intelligence; user behavior; big data analytics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Big data analytics, together with emerging technologies such as cyberphysical systems (CPS), the Internet of Things (IoT), and artificial intelligence (AI), are core elements of smart manufacturing (SM). While IoT and CPS pave the infrastructural foundation of a smart factory, the aims of big data and AI tools are to turn a large amount of industrial big data (i.e., gathered from multiple sources such as machinery sensors, integrated enterprise systems, and external/Internet platforms) into useful insights, patterns, and predictions to allow machine-to-machine collaboration, self-awareness, self-optimization, and automated decision making. In light of this, big data and AI tools are the key to realize symmetries and simulations in digital twins and smart factories.
However, due to severe data quality issues, manufacturing firms worldwide are facing substantial challenges and difficulties when developing, implementing, and utilizing big data and AI tools in their smart manufacturing initiatives. This phenomenon has become a major obstacle affecting digital symmetry of physical manufacturing entities in a virtual world and slowing down the progress of enterprise digital transformation in the Industry 4.0 era. This Special Issue provides a platform for researchers to share their latest research that investigates data quality issues in the SM context, as well as to propose and validate adequate technologies and solutions to deal with these data quality issues, and so ultimately facilitating the utilization of big data analytics and AI in smart manufacturing. We also welcome contributions and applied solutions using innovative algorithms, models, and methods to develop big data analytics and AI applications for smart factories.
Potential topics include but are not limited to the following:
- Data quality affecting symmetries in digital twins;
- Data quality issues and challenges affecting digital symmetry in smart manufacturing;
- Computational and automatic methods (e.g., data imputation and clustering analysis) to improve data quality in smart manufacturing;
- Emerging solutions and technologies to address data quality issues in smart factories;
- Big data management in smart factories;
- Data governance strategies, models, and solutions for smart manufacturing;
- Data security in smart factories;
- Cyberspace security in an IoT-based smart manufacturing environment;
- Big data, pattern behavior, and data analytics in smart manufacturing;
- Artificial intelligence and machine learning techniques for smart manufacturing;
- Industrial AI applications, prototypes, and testbeds.
All submissions should fit into the scope of journal Symmetry.
Prof. Dr. Guochao Peng
Dr. Caihua Liu
Guest Editors
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