Skip to main content
  • Book
  • © 2018

Handbook of Dynamic Data Driven Applications Systems

  • Peer-reviewed contributions that focus on the use of DDDAS for various applications:

  • Benefit: Future readers can quickly see the areas of contribution in a single (hardback volume) which can be available in libraries

  • Submissions from leading

  • experts in various domains

  • Benefit: These leaders would be identified with the text to draw in readership from the individual applications to the more general paradigm

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

  • Benefit: With the introduction and organization of the DDDAS volume, it would be the first to organize the material as to the general concepts with pictures and applications. For example, the intro chapter would outline DDDAS, the history, and applications pointed to the future chapters in the text

Buying options

eBook USD 219.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-95504-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout

This is a preview of subscription content, access via your institution.

Table of contents (32 chapters)

  1. Front Matter

    Pages i-ix
  2. Introduction to Dynamic Data Driven Applications Systems

    • Erik Blasch, Dennis Bernstein, Murali Rangaswamy
    Pages 1-25
  3. Measurement-Aware: Data Assimilation, Uncertainty Quantification

    1. Front Matter

      Pages 27-27
    2. Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems

      • Hoong C. Yeong, Ryne Beeson, N. Sri Namachchivaya, Nicolas Perkowski, Peter W. Sauer
      Pages 47-73
    3. Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awareness

      • Richard Linares, Vivek Vittaldev, Humberto C. Godinez
      Pages 75-93
  4. Signals-Aware: Process Monitoring

    1. Front Matter

      Pages 95-95
    2. Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics

      • Sida Chen, Shigeru Imai, Wennan Zhu, Carlos A. Varela
      Pages 97-121
  5. Structures-Aware: Health Modeling

    1. Front Matter

      Pages 153-153
    2. A Computational Steering Framework for Large-Scale Composite Structures

      • A. Korobenko, M.-C. Hsu, Y. Bazilevs
      Pages 155-171
    3. Dynamic Data-Driven Approach for Unmanned Aircraft Systems and Aeroelastic Response Analysis

      • R. Kania, A. Kebbie-Anthony, X. Zhao, S. Azarm, B. Balachandran
      Pages 193-211
  6. Environment-Aware: Earth, Biological, and Space Systems

    1. Front Matter

      Pages 213-213
    2. Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation

      • Haluk Damgacioglu, Emrah Celik, Chongli Yuan, Nurcin Celik
      Pages 233-252
    3. Photometric Stereopsis for 3D Reconstruction of Space Objects

      • Xue Iuan Wong, Manoranjan Majji, Puneet Singla
      Pages 253-291
  7. Situation Aware: Tracking Methods

    1. Front Matter

      Pages 293-293
    2. Aided Optimal Search: Data-Driven Target Pursuit from On-Demand Delayed Binary Observations

      • Luca Carlone, Allan Axelrod, Sertac Karaman, Girish Chowdhary
      Pages 295-335

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 in10 application areas to enable an accurate understanding, analysis, and control of complex systems; be they natural, engineered, or societal:

  • Earth and Space Data Assimilation
  • Aircraft Systems Processing
  • Structures Health Monitoring
  • Biological Data Assessment
  • Object and Activity Tracking
  • Embedded Control and Coordination
  • Energy-Aware Optimization
  • Image and Video Computing
  • Security and Policy Coding
  • Systems Design

 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.



  • 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

Editors and Affiliations

  • Air Force Office of Scientific Research, Air Force Research Laboratory, Arlington, USA

    Erik Blasch

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

    Sai Ravela

  • Information Directorate, Air Force Research Laboratory, Rome, USA

    Alex Aved

About the editors

Dr. 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. Recognitions include the Military Sensing Society Mignogna leadership in data fusion award, IEEE Aerospace and Electronics Systems Society Mimno best magazine paper award, IEEE Russ bioengineering award, and founding member of the International Society of Information Fusion (ISIF). 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 AIAA, Fellow of SPIE, and Fellow of IEEE.


Dr. Sai Ravela directs the Earth Signals and Systems Group with research interests in Dynamic Data Driven Observing Systems at the Massachusetts Institute of Technology (MIT). He has made key contributions to Dynamic Data Driven cooperative autonomous observation of fluids, atmosphere, wildlife, retail intelligence, and micro-positioning radar. He has pioneered DDDAS concepts, and organized the first three DDDAS conferences that form the basis of this book. He has over 100 publications and patents, is the co-founder of Windrisktech LLC and E5 Aerospace LLC, and is a recipient of the MIT Infinite Kilometer award for exceptional research and outstanding mentorship.


Dr. 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

Buying options

eBook USD 219.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-95504-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout