Handbook of dynamic data driven applications systems. Volume 1 / Erik P. Blasch, Frederica Darema, Sai Ravela, Alex J. Aved, editors.
Blasch, Erik, editor.; Darema, Frederica, editor.; Ravela, Sai, editor.; Aved, Alex J., editor.
2022
QA76.9.C65
Available Online

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Title Handbook of dynamic data driven applications systems. Volume 1 / Erik P. Blasch, Frederica Darema, Sai Ravela, Alex J. Aved, editors.
Edition Second edition.
ISBN 9783030745684 (electronic bk.)
3030745686 (electronic bk.)
9783030745677
3030745678
DOI https://doi.org/10.1007/978-3-030-74568-4
Published Cham : Springer, 2022.
Language English
Description 1 online resource (1 volume) : illustrations (black and white, and colour).
Other Standard Identifiers 10.1007/978-3-030-74568-4 doi
Call Number QA76.9.C65
Dewey Decimal Classification 003.3
Summary 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.
Note Previous edition: 2018.
Includes index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Blasch, Erik, editor.
Darema, Frederica, editor.
Ravela, Sai, editor.
Aved, Alex J., editor.
Available in Other Form Handbook of dynamic data driven applications systems. Volume 1.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
1 Introduction to Dynamic Data Driven Applications Systems
2 Tractable Non-Gaussian Representation in Dynamic Data Driven Coherent Fluid Mapping
3 Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems
4 Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awareness
5 Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics
6 Markov Modeling of Time Series via Spectral Analysis for Detection of Combustion Instabilities
7 Dynamic Space-Time Model for Syndromic Surveillance with Particle Filters and Dirichlet Process
8 A Computational Steering Framework for Large-Scale Composite Structures
9 Development of Intelligent and Predictive Self-Healing Composite Structures using Dynamic Data-Driven Applications Systems
10 Dynamic Data-Driven Approach for Unmanned Aircraft Systems aero-elastic response analysis
11 Transforming Wildfire Detection and Prediction using New and Underused Sensor and Data Sources Integrated with Modeling
12 Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation
13 Photometric Steropsis for 3D Reconstruction of Space Objects
14 Aided Optimal Search: Data-Driven Target Pursuit from On-Demand Delayed Binary Observations
15 Optimization of Multi-Target Tracking within a Sensor Network via Information Guided Clustering
16 Data-Driven Prediction of Confidence for EVAR in Time-varying Datasets
17 DDDAS for Attack Detection and Isolation of Control Systems
18 Approximate Local Utility Design for Potential Game Approach to Cooperative Sensor Network Planning
19 Dynamic Sensor-Actor Interactions for Path-Planning in a Threat Field
20 Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction
21 A Dynamic Data-Driven Optimization Framework for Demand Side Management in Microgrids
22 Dynamic Data Driven Partitioning of Smart Grid Using Learning Methods
23 Design of a Dynamic Data-Driven System for Multispectral Video Processing
24 Light Field Image Compression
25 On Compression of Machine-derived Context Sets for Fusion of Multi-model Sensor Data
26 Simulation-based Optimization as a Service for Dynamic Data-driven Applications Systems
27 Privacy and Security Issues in DDDAS Systems
28 Dynamic Data Driven Application Systems (DDDAS) for Multimedia Content Analysis
29 Parzen Windows: Simplest Regularization Algorithm
30 Multiscale DDDAS Framework for Damage Prediction in Aerospace Composite Structures
31 A Dynamic Data-Driven Stochastic State-awareness Framework for the Next Generation of Bio-inspired Fly-by-feel Aerospace Vehicles
DDDAS: The Way Forward. .

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