Regulating Industrial Internet Through IPR, Data Protection and Competition Law

Regulating Industrial Internet Through IPR, Data Protection and Competition Law
Author: Rosa Maria Ballardini
Publisher: Kluwer Law International B.V.
Total Pages: 517
Release: 2019-08-28
Genre: Law
ISBN: 9403503416
GET BOOK

The digitization of industrial processes has suddenly taken a great leap forward, with burgeoning applications in manufacturing, transportation and numerous other areas. Many stakeholders, however, are uncertain about the opportunities and risks associated with it and what it really means for businesses and national economies. Clarity of legal rules is now a pressing necessity. This book, the first to deal with legal questions related to Industrial Internet, follows a multidisciplinary approach that is instructed by law concerning intellectual property, data protection, competition, contracts and licensing, focusing on business, technology and policy-driven issues. Experts in various relevant fields of science and industry measure the legal tensions created by Industrial Internet in our global economy and propose solutions that are both theoretically valuable and concretely practical, identifying workable business models and practices based on both technical and legal knowledge. Perspectives include the following: regulating Industrial Internet via intellectual property rights (IPR); data ownership versus control over data; artificial intelligence and IPR infringement; patent owning in Industrial Internet; abuse of dominance in Industrial Internet platforms; data collaboration, pooling and hoarding; legal implications of granular versioning technologies; and misuse of information for anticompetitive purposes. The book represents a record of a major collaborative project, held between 2016 and 2019 in Finland, involving a number of universities, technology firms and law firms. As Industrial Internet technologies are already being used in several businesses, it is of paramount importance for the global economy that legal, business and policy-related challenges are promptly analyzed and discussed. This crucially important book not only reveals the legal and policy-related issues that we soon will have to deal with but also facilitates the creation of legislation and policies that promote Industrial-Internet-related technologies and new business opportunities. It will be warmly welcomed by practitioners, patent and other IPR attorneys, innovation economists and companies operating in the Industrial Internet ecosystem, as well as by competition authorities and other policymakers.

The Data Industry

The Data Industry
Author: Chunlei Tang
Publisher: John Wiley & Sons
Total Pages: 217
Release: 2016-06-13
Genre: Mathematics
ISBN: 111913840X
GET BOOK

Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.

Practical Industrial Data Networks

Practical Industrial Data Networks
Author: Steve Mackay
Publisher: Elsevier
Total Pages: 439
Release: 2004-02-27
Genre: Technology & Engineering
ISBN: 0080480217
GET BOOK

There are many data communications titles covering design, installation, etc, but almost none that specifically focus on industrial networks, which are an essential part of the day-to-day work of industrial control systems engineers, and the main focus of an increasingly large group of network specialists.The focus of this book makes it uniquely relevant to control engineers and network designers working in this area. The industrial application of networking is explored in terms of design, installation and troubleshooting, building the skills required to identify, prevent and fix common industrial data communications problems - both at the design stage and in the maintenance phase.The focus of this book is 'outside the box'. The emphasis goes beyond typical communications issues and theory to provide the necessary toolkit of knowledge to solve industrial communications problems covering RS-232, RS-485, Modbus, Fieldbus, DeviceNet, Ethernet and TCP/IP. The idea of the book is that in reading it you should be able to walk onto your plant, or facility, and troubleshoot and fix communications problems as quickly as possible. This book is the only title that addresses the nuts-and-bolts issues involved in design, installation and troubleshooting that are the day-to-day concern of engineers and network specialists working in industry.* Provides a unique focus on the industrial application of data networks * Emphasis goes beyond typical communications issues and theory to provide the necessary toolkit of knowledge to solve industrial communications problems* Provides the tools to allow engineers in various plants or facilities to troubleshoot and fix communications problems as quickly as possible

Big Data for Twenty-First-Century Economic Statistics

Big Data for Twenty-First-Century Economic Statistics
Author: Katharine G. Abraham
Publisher: University of Chicago Press
Total Pages: 502
Release: 2022-03-11
Genre: Business & Economics
ISBN: 022680125X
GET BOOK

Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.

Multivariable System Identification For Process Control

Multivariable System Identification For Process Control
Author: Y. Zhu
Publisher: Elsevier
Total Pages: 373
Release: 2001-10-08
Genre: Technology & Engineering
ISBN: 0080537111
GET BOOK

Systems and control theory has experienced significant development in the past few decades. New techniques have emerged which hold enormous potential for industrial applications, and which have therefore also attracted much interest from academic researchers. However, the impact of these developments on the process industries has been limited.The purpose of Multivariable System Identification for Process Control is to bridge the gap between theory and application, and to provide industrial solutions, based on sound scientific theory, to process identification problems. The book is organized in a reader-friendly way, starting with the simplest methods, and then gradually introducing more complex techniques. Thus, the reader is offered clear physical insight without recourse to large amounts of mathematics. Each method is covered in a single chapter or section, and experimental design is explained before any identification algorithms are discussed. The many simulation examples and industrial case studies demonstrate the power and efficiency of process identification, helping to make the theory more applicable. MatlabTM M-files, designed to help the reader to learn identification in a computing environment, are included.

Intelligent Methods and Big Data in Industrial Applications

Intelligent Methods and Big Data in Industrial Applications
Author: Robert Bembenik
Publisher: Springer
Total Pages: 370
Release: 2018-05-18
Genre: Technology & Engineering
ISBN: 3319776045
GET BOOK

The inspiration for this book came from the Industrial Session of the ISMIS 2017 Conference in Warsaw. It covers numerous applications of intelligent technologies in various branches of the industry. Intelligent computational methods and big data foster innovation and enable the industry to overcome technological limitations and explore the new frontiers. Therefore it is necessary for scientists and practitioners to cooperate and inspire each other, and use the latest research findings to create new designs and products. As such, the contributions cover solutions to the problems experienced by practitioners in the areas of artificial intelligence, complex systems, data mining, medical applications and bioinformatics, as well as multimedia- and text processing. Further, the book shows new directions for cooperation between science and industry and facilitates efficient transfer of knowledge in the area of intelligent information systems.