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Resilience in the Digital Age 1st ed. 2021 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 199 pages, height x width: 235x155 mm, weight: 454 g, 35 Illustrations, color; 8 Illustrations, black and white; XII, 199 p. 43 illus., 35 illus. in color., 1 Paperback / softback
  • Sērija : Information Systems and Applications, incl. Internet/Web, and HCI 12660
  • Izdošanas datums: 20-Feb-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 303070369X
  • ISBN-13: 9783030703691
  • Mīkstie vāki
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  • Formāts: Paperback / softback, 199 pages, height x width: 235x155 mm, weight: 454 g, 35 Illustrations, color; 8 Illustrations, black and white; XII, 199 p. 43 illus., 35 illus. in color., 1 Paperback / softback
  • Sērija : Information Systems and Applications, incl. Internet/Web, and HCI 12660
  • Izdošanas datums: 20-Feb-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 303070369X
  • ISBN-13: 9783030703691
The growth of a global digital economy has enabled rapid communication, instantaneous movement of funds, and availability of vast amounts of information. With this come challenges such as the vulnerability of digitalized sociotechnological systems (STSs) to destructive events (earthquakes, disease events, terrorist attacks). Similar issues arise for disruptions to complex linked natural and social systems (from changing climates, evolving urban environments, etc.). This book explores new approaches to the resilience of sociotechnological and natural-social systems in a digital world of big data, extraordinary computing capacity, and rapidly developing methods of Artificial Intelligence.





Most of the books papers were presented at the Workshop on Big Data and Systems Analysis held at the International Institute for Applied Systems Analysis in Laxenburg, Austria in February, 2020. Their authors are associated with the Task Group Advanced mathematical tools for data-driven applied systems analysis created and sponsored by CODATA in November, 2018.





The world-wide COVID-19 pandemic illustrates the vulnerability of our healthcare systems, supply chains, and social infrastructure, and confronts our notions of what makes a system resilient. We have found that use of AI tools can lead to problems when unexpected events occur. On the other hand, the vast amounts of data available from sensors, satellite images, social media, etc. can also be used to make modern systems more resilient.





Papers in the book explore disruptions of complex networks and algorithms that minimize departure from a previous state after a disruption; introduce a multigrammatical framework for the technological and resource bases of todays large-scale industrial systems and the transformations resulting from disruptive events; and explain how robotics can enhance pre-emptive measures or post-disaster responses to increase resiliency. Other papers explore current directions in data processing and handling and principles of FAIRness in data; how the availability of large amounts of data can aid in the development of resilient STSs and challenges to overcome in doing so. The book also addresses interactions between humans and built environments, focusing on how AI can inform todays smart and connected buildings and make them resilient, and how AI tools can increase resilience to misinformation and its dissemination.
Resilience of Socio Technological Systems
Chapter 1 Resilience Algorithms in Complex Networks
3(13)
Fred S. Roberts
Chapter 2 Application of the Multigrammatical Framework to the Assessment of Resilience and Recoverability of Large - Scale Industrial Systems
16(19)
Igor A. Sheremet
Chapter 3 Vulnerability Assessment of Digitized Socio-technological Systems via Entropy
35(10)
Hossein Hassani
Nadejda Komendantova
Stephan Unger
Chapter 4 Two-Stage Nonsmooth Stochastic Optimization and Iterative Stochastic Quasigradient Procedure for Robust Estimation, Machine Learning and Decision Making
45(30)
Tatiana Ermolieva
Yuri Ermoliev
Michael Obersteiner
Elena Rovenskaya
Chapter 5 Robotics as an Enabler of Resiliency to Disasters: Promises and Pitfalls
75(30)
Rui Wang
Daniel Nakhimovich
Fred S. Roberts
Kostas E. Bekris
Data Science and Resilience
Chapter 6 Big Data and FAIR Data for Data Science
105(13)
Alexei Gvishiani
Michael Dobrovolsky
Alena Rybkina
Chapter 7 Data Science and Resilience
118(21)
Fred S. Roberts
Chapter 8 Building Resilience into the Metadata-Based ETL Process Using Open Source Big Data Technologies
139(18)
Peter Panfilov
Alexander Suleykin
Applications
Chapter 9 Towards Democratizing Human-Building Simulation and Analytics
157(15)
Muhammad Usman
Brandon Haworth
Glen Berseth
Petros Faloutsos
Mubbasir Kapadia
Chapter 10 The Adequacy of Artificial Intelligence Tools to Combat Misinformation
172(27)
Nadejda Komendantova
Love Ekenberg
Wolfgang Amann
Mats Danielson
Vasilis Koulolias
Author Index 199