International Journal of Communications, Network and System Sciences

International Journal of Communications, Network and System Sciences

ISSN Print: 1913-3715
ISSN Online: 1913-3723
www.scirp.org/journal/ijcns
E-mail: ijcns@scirp.org
"Big Data Analysis in Smart Manufacturing: A Review"
written by Kevin Nagorny, Pedro Lima-Monteiro, Jose Barata, Armando Walter Colombo,
published by International Journal of Communications, Network and System Sciences, Vol.10 No.3, 2017
has been cited by the following article(s):
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[14] Model-based Big Data Analytics-as-a-Service framework in smart manufacturing: A case study
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[15] Smart manufacturing powered by recent technological advancements: A review
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[17] Assembly line overall equipment effectiveness (OEE) prediction from human estimation to supervised machine learning
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[18] Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective
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[19] A Data Warehouse-Based System for Service Customization Recommendations in Product-Service Systems
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[20] Enabling energy‐efficient manufacturing of pharmaceutical solid oral dosage forms via integrated techno‐economic analysis and advanced process modeling
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[21] Data Communication-Edge, Fog, and Cloud Computing
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[22] Factors Affecting Information Technology Professionals' Decisions to Adopt Big Data Analytics Among Small-and Medium-Sized Enterprises: A Quantitative Study
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[23] Application of Mathematical Economic Model in Financial System in Manufacturing Industry
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[24] Evaluation of smart manufacturing performance using a grey theory-based approach: a case study
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[25] Understanding and Defining Dark Data for the Manufacturing Industry
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[26] Machine Learning Methods for Product Quality Monitoring in Electric Resistance Welding
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[27] A Preliminary Overview of the Situation in Big Data Testing
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[28] A Preliminary Overview of the Situation in Big Data Testing.
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[29] Smart factory: security issues, challenges, and solutions
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[30] Quality Assurance in Big Data Engineering-A Metareview
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[32] Data analytics using statistical methods and machine learning: a case study of power transfer units
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[33] Datadriven Human Intention Analysis
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[34] Big Data Needs and Challenges in Smart Manufacturing: An Industry-Academia Survey
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[35] Becoming Digital: The Need to Redesign Competences and Skills in the Fashion Industry
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[36] A Cyber-Physical Data Management and Analytics System (CP-DMAS) for Smart Factories
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[37] An Ontology for Operator 4.0 based on Interoperability of Industrie 4.0 Reference Architectures with FIWARE
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[38] Manufacturing Audit Quality Analysis Model Based on Data Mining Technology
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[39] Development of a supervisory internet of things (IoT) system for factories of the future
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[40] Human Movement Direction Prediction using Virtual Reality and Eye Tracking
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[41] Applying Test Driven Development in the Big Data Domain–Lessons From the Literature
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[42] A value-add driven report development framework for mining industries
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[43] On assessing grindability of recycled and ore-based crankshaft steel: an approach combining data analysis with material science
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[45] Analytic optimization framework for resilient manufacturing production and supply planning in Industry 4.0 context-buffer stock allocation-case study
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[49] Design and deployment of an analytic artefact–investigating mechanisms for integrating analytics and manufacturing execution system
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[50] Decision Support System for Improved Operations, Maintenance, and Safety: a Data-Driven Approach
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[52] A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future
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[53] A Deep Learning-Based Model for the Automated Assessment of the Activity of a Single Worker
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[54] Integration of data analytics with cloud services for safer process systems, application examples and implementation challenges
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[55] First Time Quality Diagnostics and Improvement through Data Analysis: A Study of a Crankshaft Line
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[56] The Influence of Smart Manufacturing Towards Energy Conservation: A Review
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[57] Recognizing the Necessity for Developing Customer-Oriented New Products for the 4th Industrial Revolution
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[58] Human behavior understanding for worker-centered intelligent manufacturing
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[59] Intelligent Maintenance Systems and Predictive Manufacturing
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[60] Requirements for Big Data Adoption for Railway Asset Management
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[61] A Systematic Review of Big Data Analytics for Oil and Gas Industry 4.0
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[62] Machine Learning and Data Mining in Manufacturing
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[63] Discussing Relations Between Dynamic Business Environments and Big Data Analytics
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[64] Manufacturing big data ecosystem: A systematic literature review
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[65] A Holistic Approach for Selecting Appropriate Manufacturing Shop Floor KPIs
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[66] Exploring the applicability of test driven development in the big data domain
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[67] Evaluating the impact of operational reports
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[68] Human movement direction classification using virtual reality and eye tracking
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[69] Big Data
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[70] Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios
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[71] 4 차 산업혁명 시대를 대비한 고객 중심의 신제품 개발 필요성 인식 제고
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[72] The Industry 4.0 Knowledge & Technology Framework
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[73] Determination of Problems in Transition to Smart Manufacturing Model and Suggestions for Enterprises
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[75] A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: a framework, challenges and future research …
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[76] A framework to help decision makers to be environmentally aware during the maintenance of cyber physical systems
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[77] Multi-modal recognition of worker activity for human-centered intelligent manufacturing
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[78] Generation of project solutions on choosing of mechanical assembly production of the Industry 4.0 using operators of genetic algorithms
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[79] Cognitive IoT for Smart Environment: A survey on Enabling Technologies, Architectures, Approaches and Research Challenges
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[80] An FCM–GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template
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[81] Smart manufacturing systems: state of the art and future trends
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[82] Investigation of Fusion Features for Apple Classification in Smart Manufacturing
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[83] Building a Simple Smart Factory
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[84] 驱动制造业从 “互联网+” 走向 “人工智能+” 的大数据之道
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[85] Finding The Four Qualities Of Intelligent Industrial Reporting
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[86] Smart factory: A methodology for adaptation
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[87] Transformation towards smart factory system: Examining new job profiles and competencies
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[88] State and Trends of Machine Learning Approaches in Business: An Empirical Review
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[89] Recommendation Framework for on-Demand Smart Product Customization
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[90] Recommendation Framework for on-Demand Smart Product Customization.
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[91] Exploring the Specificities and Challenges of Testing Big Data Systems
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[92] 面向生产管控的工业大数据研究及应用
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[93] On Big Data Driving Manufacturing from “Internet Plus” to “AI Plus”
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[94] Comparison of Machine Learning Approaches for Time-series-based Quality Monitoring of Resistance Spot Welding (RSW)
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[96] A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry
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[97] A Survey on the Concepts, Trends, Enabling Technologies, Architectures, Challenges and Open Issues in Cognitive IoT Based Smart Environments
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[98] Innovation and entrepreneurship guidance system based on clustering algorithm
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[99] Worker Activity Recognition in Smart Manufacturing Using IMU and sEMG Signals with Convolutional Neural Networks
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[100] Knowledge integration via the fusion of the data models used in automotive production systems
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[101] Semantical support for a CPS data marketplace to prepare Big Data analytics in smart manufacturing environments
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[102] Big Data: Concept, Potentialities and Vulnerabilities
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[103] Data Mining for Material Feeding Optimization of Printed Circuit Board Template Production
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[104] When Smart Gets Smarter: How Big Data Analytics Creates Business Value in Smart Manufacturing
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