Handbook of Dynamic Data Driven Applications Systems: Volume 1, 2nd Edition

Handbook of Dynamic Data Driven Applications Systems: Volume 1 2nd ed. 2022 Edition
Author: Erik P. Blasch (Editor), Frederica Darema (Editor), Sai Ravela (Editor), Alex J. Aved (Editor)
Publisher Finelybook 出版社:Springer; 2nd ed. 2022 edition (May 12, 2022)
Language 语言:English
pages 页数:776 pages
ISBN-10 书号:3030745678
ISBN-13 书号:9783030745677

Book Description
The Handbook of Dynamic Data Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered Author: 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 Author: 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

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