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  • © 2022

Handbook of Dynamic Data Driven Applications Systems

Volume 1

  • Peer-reviewed contributions that focus on the use of DDDAS for various applications into one volume

  • Contributions from leading experts in various domains to reflect individual applications to the more general paradigm

  • Identification of contemporary concepts using DDDAS such as UAVs, surveillance, and computing

  • 5514 Accesses

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eBook USD 219.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-74568-4
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  • Readable on all devices
  • Own it forever
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  • Tax calculation will be finalised during checkout
Hardcover Book USD 279.99
Price excludes VAT (USA)

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Table of contents (32 chapters)

  1. Situation Aware: Tracking Methods

    1. Data-Driven Prediction of Confidence for EVAR in Time-Varying Datasets

      • Allan Axelrod, Luca Carlone, Girish Chowdhary, Sertac Karaman
      Pages 389-412
  2. Context-Aware: Coordinated Control

    1. Front Matter

      Pages 413-413
    2. DDDAS for Attack Detection and Isolation of Control Systems

      • Luis Francisco Combita, Jairo Alonso Giraldo, Alvaro A. Cardenas, Nicanor Quijano
      Pages 415-430
    3. Dynamic Sensor-Actor Interactions for Path-Planning in a Threat Field

      • Benjamin S. Cooper, Raghvendra V. Cowlagi
      Pages 453-472
  3. Energy-Aware: Power Systems

    1. Front Matter

      Pages 473-473
    2. Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction

      • Michael Hunter, Aradhya Biswas, Bhargava Chilukuri, Angshuman Guin, Richard Fujimoto, Randall Guensler et al.
      Pages 475-495
    3. A Dynamic Data-Driven Optimization Framework for Demand Side Management in Microgrids

      • Haluk Damgacioglu, Mehrad Bastani, Nurcin Celik
      Pages 497-512
    4. Dynamic Data Driven Partitioning of Smart Grid for Improving Power Efficiency by Combinining K-Means and Fuzzy Methods

      • Antonia Nasiakou, Miltiadis Alamaniotis, Lefteri H. Tsoukalas, Manolis Vavalis
      Pages 513-535
  4. Process-Aware: Image and Video Coding

    1. Front Matter

      Pages 537-537
    2. Design of a Dynamic Data-Driven System for Multispectral Video Processing

      • Honglei Li, Yanzhou Liu, Kishan Sudusinghe, Jinsung Yoon, Erik P. Blasch, Mihaela van der Schaar et al.
      Pages 539-556
  5. Cyber-Aware: Security and Computing

    1. Front Matter

      Pages 601-601
    2. Simulation-Based Optimization as a Service for Dynamic Data-Driven Applications Systems

      • Yi Li, Shashank Shekhar, Yevgeniy Vorobeychik, Xenofon Koutsoukos, Aniruddha Gokhale
      Pages 603-627
    3. Privacy and Security Issues in DDDAS Systems

      • Li Xiong, Vaidy Sunderam, Liyue Fan, Slawomir Goryczka, Layla Pournajaf
      Pages 629-644
    4. Multimedia Content Analysis with Dynamic Data Driven Applications Systems (DDDAS)

      • Erik P. Blasch, Alex J. Aved, Shuvra S. Bhattacharyya
      Pages 645-667
  6. Systems-Aware: Design Methods

    1. Front Matter

      Pages 669-669
    2. Parzen Windows: Simplest Regularization Algorithm

      • Jing Peng, Peng Zhang
      Pages 671-692

About this book

The Handbook of Dynamic Data Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered by Dr. Darema and the co-authors for researchers and practitioners developing DDDAS technologies.

Beginning with general concepts and history of the paradigm, the text provides 32 chapters by leading experts in ten application areas to enable an accurate understanding, analysis, and control of complex systems; be they natural, engineered, or societal:

The authors explain how DDDAS unifies the computational and instrumentation aspects of an application system, extends the notion of Smart Computing to span from the high-end to the real-time data acquisition and control, and manages Big Data exploitation with high-dimensional model coordination.



The Dynamically Data Driven Applications Systems (DDDAS) paradigm inspired research regarding the prediction of severe storms.  Specifically, the DDDAS concept allows atmospheric observing systems, computer forecast models, and cyberinfrastructure to dynamically configure themselves in optimal ways in direct response to current or anticipated weather conditions.  In so doing, all resources are used in an optimal manner to maximize the quality and timeliness of information they provide.

                                            Kelvin Droegemeier, Regents’ Professor of Meteorology at the University of Oklahoma; former Director of the White House Office of Science and Technology Policy

                                          

We may well be entering the golden age of data science, as society in general has come to appreciate the possibilities for organizational strategies that harness massive streams of data. The challenges and opportunities are even greater when the data or the underlying system are dynamic - and DDDAS is the time-tested paradigm for realizing this potential.

                          Sangtae Kim, Distinguished Professor of Mechanical Engineering and Distinguished Professor of Chemical Engineering at Purdue University

Keywords

  • DDDAS
  • Controls
  • Instrumentation
  • Big Data
  • High performance computing
  • Cyber physical systems
  • UAVs
  • data fusion
  • feature fusion
  • decision fusion
  • information fusion
  • Environmental Modeling
  • Environmental Analysis
  • Architectures
  • Statistical modeling
  • data assimilation

Reviews

The Dynamically Data Driven Applications Systems (DDDAS) paradigm inspired research regarding the prediction of severe storms.  Specifically, the DDDAS concept allows atmospheric observing systems, computer forecast models, and cyberinfrastructure to dynamically configure themselves in optimal ways in direct response to current or anticipated weather conditions.  In so doing, all resources are used in an optimal manner to maximize the quality and timeliness of information they provide.

            Kelvin Droegemeier, Regents’ Professor of Meteorology at the University of Oklahoma; former Director of the White House Office of Science and Technology Policy

             We may well be entering the golden age of data science, as society in general has come to appreciate the possibilities for organizational strategies that harness massive streams of data. The challenges and opportunities are even greater when the data or the underlying system are dynamic - and DDDAS is the time-tested paradigm for realizing this potential.

             Sangtae Kim, Distinguished Professor of Mechanical Engineering and Distinguished Professor of Chemical Engineering at Purdue University

 

Editors and Affiliations

  • Air Force Office of Scientific Research, Arlington, USA

    Erik P. Blasch

  • InfoSymbiotics Systems Society, Boston, USA

    Frederica Darema

  • Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, USA

    Sai Ravela

  • Air Force Research Lab, Rome, USA

    Alex J. Aved

About the editors

Erik P. Blasch is a program officer with the Air Force Office of Scientific Research. His focus areas are in multi-domain (space, air, ground) data fusion, target tracking, pattern recognition, and robotics. He has authored 750+ scientific papers, 22 patents, 30 tutorials, and 5 books. His recognitions include the Military Sensing Society Mignogna Leadership in Data Fusion Award, IEEE Aerospace and Electronics Systems Society Mimno Best Magazine Paper Award, and IEEE Russ Bioengineering Award. He was also a founding member of the International Society of Information Fusion (ISIF). His previous appointments include adjunct associate professor at Wright State University, exchange scientist at Defense Research and Development Canada, and officer in the Air Force Research Laboratory. Dr. Blasch is an associate fellow of American Institute of Aeronautics and Astronautics (AIAA), fellow of the Society of Photo-Optical and Instrumentation Engineers (SPIE) and fellow of the Institute of Electrical and Electronics Engineers (IEEE).

Dr. Frederica Darema: retired as Senior Executive Service (SES) member and Director of the Air Force Office of Scientific Research, in Arlington, Virginia, where she led the entire basic research investment for the AF and served as Research Director in the Air Force’s Chief Data Office, and as Associate Deputy Assistant Secretary the Air Force Office for Science, Technology and Engineering.  Prior career history includes: research staff positions at the University of Pittsburgh, Brookhaven National Laboratory, and Schlumberger-Doll; and management and executive-level positions at: the T.J.Watson IBM Research Center and the IBM Corporate Strategy Group; the National Science Foundation and the Defense Advanced Research Projects Agency; and Director of the AFOSR Directorate for Information, Math, and Life Sciences. Dr. Darema, PhD in Nuclear Physics, is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), among other professional recognitions. She pioneered the DDDAS paradigm and since 2000 has organized and led research initiatives, programs, workshops, conferences, and other forums, to foster and promote DDDAS-based science and technology advances.

Dr. Ravela, Ph.D. 2002, directs the Earth Signals and Systems Group (ESSG) in the Earth Atmospheric and Planetary Sciences (EAPS) department at the Massachusetts Institute of Technology. His primary interests are in statistical pattern recognition, stochastic nonlinear systems, and computational intelligence with application to earth, planets, climate, and life. Dr. Ravela has pioneered dynamic data driven observing systems for wildlife and fluids, the latter with application from the laboratory to localized atmospheric phenomena. He has advanced several DDDAS topics with new methods for application to coherent fluid dynamical regimes. Dr. Ravela proposed and co-organized the Dynamic Data Driven Environmental Systems Science Conference (DyDESS 2014, Cambridge), and then co-organized the first, second, and third general DDDAS conferences (2016 Hartford, 2017 Cambridge, 2020 MIT/Online). Dr. Ravela also teaches Machine Learning with System Dynamics and Optimization, which introduces the informative approach, a key DDDAS concept, to design Learning and Hybrid Stochastic Systems and solve inverse problems and inference.

Alex J. Aved is a senior researcher with the Air Force Research Laboratory, Information Directorate, Rome, NY, USA. His research interests include multimedia databases, stream processing (via CPU, GPU, or coprocessor), and dynamically executing models with feedback loops incorporating measurement and error data to improve the accuracy of the model. He has published over 50 papers and given numerous invited lectures. Previously he was a programmer at the University of Central Florida and database administrator and programmer at Anderson University.

Bibliographic Information

  • Book Title: Handbook of Dynamic Data Driven Applications Systems

  • Book Subtitle: Volume 1

  • Editors: Erik P. Blasch, Frederica Darema, Sai Ravela, Alex J. Aved

  • DOI: https://doi.org/10.1007/978-3-030-74568-4

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2022

  • Hardcover ISBN: 978-3-030-74567-7

  • eBook ISBN: 978-3-030-74568-4

  • Edition Number: 2

  • Number of Pages: X, 766

  • Number of Illustrations: 41 b/w illustrations, 228 illustrations in colour

  • Topics: Computer Modelling, Computer and Information Systems Applications

Buying options

eBook USD 219.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-74568-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 279.99
Price excludes VAT (USA)