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Big Data in Context: Legal, Social and Technological Insights

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Book Series: SpringerBriefs in Law ISSN: 2192-8568 ISBN: 9783319624600 9783319624617 Year: Pages: 120 DOI: https://doi.org/10.1007/978-3-319-62461-7 Language: English
Publisher: Springer Nature
Subject: Information theory
Added to DOAB on : 2017-11-24 10:19:35
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This book sheds new light on a selection of big data scenarios from an interdisciplinary perspective. It features legal, sociological and economic approaches to fundamental big data topics such as privacy, data quality and the ECJ’s Safe Harbor decision on the one hand, and practical applications such as smart cars, wearables and web tracking on the other. Addressing the interests of researchers and practitioners alike, it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.All contributions are based on papers submitted in connection with ABIDA (Assessing Big Data), an interdisciplinary research project exploring the societal aspects of big data and funded by the German Federal Ministry of Education and Research.This volume was produced as a part of the ABIDA project (Assessing Big Data, 01IS15016A-F). ABIDA is a four-year collaborative project funded by the Federal Ministry of Education and Research. However the views and opinions expressed in this book reflect only the authors’ point of view and not necessarily those of all members of the ABIDA project or the Federal Ministry of Education and Research.

The Big Data Agenda

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Book Series: Critical, Digital and Social Media Studies ISBN: 9781911534723 9781911534976 9781911534730 9781911534747 9781911534754 Year: Pages: 154 DOI: 10.16997/book14 Language: English
Publisher: University of Westminster Press Grant: Knowledge Unlatched - 101385
Subject: Biology --- Internal medicine --- Medicine (General) --- Philosophy --- Media and communication
Added to DOAB on : 2018-05-09 11:01:56
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"This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raises significant ethical issues. Annika Richterich provides a systematic contemporary overview of the field of critical data studies that reflects on practices of digital data collection and analysis. The book assesses in detail one big data research area: biomedical studies, focused on epidemiological surveillance. Specific case studies explore how big data have been used in academic work. The Big Data Agenda concludes that the use of big data in research urgently needs to be considered from the vantage point of ethics and social justice. Drawing upon discourse ethics and critical data studies, Richterich argues that entanglements between big data research and technology/ internet corporations have emerged. In consequence, more opportunities for discussing and negotiating emerging research practices and their implications for societal values are needed. An electronic version of this book is freely available, thanks to the support of libraries working with Knowledge Unlatched. KU is a collaborative initiative designed to make high quality books Open Access for the public good. More information about the initiative and details about KU's Open Access programme can be found at www.knowledgeunlatched.org."

New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes

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ISBN: 9783039282906 9783039282913 Year: Pages: 428 DOI: 10.3390/books978-3-03928-291-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Industrial Engineering
Added to DOAB on : 2020-04-07 23:07:09
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Modern factories are experiencing rapid digital transformation supported by emerging technologies, such as the Industrial Internet of things (IIOT), industrial big data and cloud technologies, deep learning and deep analytics, AI, intelligent robotics, cyber-physical systems and digital twins, complemented by visual computing (including new forms of artificial vision with machine learning, novel HMI, simulation, and visualization). This is evident in the global trend of Industry 4.0. The impact of these technologies is clear in the context of high-performance manufacturing. Important improvements can be achieved in productivity, systems reliability, quality verification, etc. Manufacturing processes, based on advanced mechanical principles, are enhanced by big data analytics on industrial sensor data. In current machine tools and systems, complex sensors gather useful data, which is captured, stored, and processed with edge, fog, or cloud computing. These processes improve with digital monitoring, visual data analytics, AI, and computer vision to achieve a more productive and reliable smart factory. New value chains are also emerging from these technological changes. This book addresses these topics, including contributions deployed in production, as well as general aspects of Industry 4.0.

Keywords

cutting insert selection --- cutting parameter optimization --- artificial neural networks --- genetic algorithm --- connected enterprise --- smart manufacturing --- big data --- machine learning --- data reduction --- predictive analytics --- in-line dimensional inspection --- warm forming --- 3D mesh reconstruction --- optical system --- revolution workpiece --- defect detection --- polymer lithium-ion battery --- convolutional neural network --- deep learning --- blister defect --- flower pollination algorithm --- Industry 4.0 --- anomaly detection --- scheduling --- neural network --- skyline queries --- Cyber-Physical Systems (CPS) --- scalability test --- Internet of Things (IoT) --- INDUSTRY 4.0 --- economic recession --- research and development indicators --- maintenance expert --- competence --- decision support --- micro-armature --- defect detection --- convolutional neural networks --- computer vision --- capacity control --- job shop systems --- RMTs --- operator theory --- 4th industrial revolution --- industry 4.0 --- AHP --- QFD --- matching --- fibre of preserved Szechuan pickle --- contour detection --- dilated convolutions --- HED --- social network --- industry 4.0 --- industrial knowledge graph --- deep learning --- industrial big data --- intellectualization of industrial information --- digital manufacturing --- smart factory --- Industry 4.0 --- digital platforms --- automated surface inspection --- D-VGG16 --- bilinear model --- Grad-CAM --- classification --- localization --- elliptical paraboloid array --- self-calibration method --- vertex distance --- optical slope sensor --- geometric relationship --- relative angle --- fabric defect detection --- LGM --- FCM --- image smoothing --- Industry 4.0 --- marketing innovations --- innovative marketing tools --- impacts marketing innovations --- Industry 4.0 --- configure-to-order --- BIM --- construction equipment --- digital information flow --- lean assembly --- digital twins --- cyber-physical production systems --- depthwise separable convolution --- YOLOv3 --- feature pyramid --- aircraft structure crack detection --- industrial load management --- demand-side management --- demand-side response --- energy flexibility --- IT concept --- platform-based ecosystem --- control service --- smart service --- control as a service --- cloud-based control system --- automation system --- chatter --- train wheel --- smart system --- turning --- edge computing --- n/a

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