Dynamic Data-Driven Application Systems



Dynamic Data-Driven Applications

This is a scientific community web site dedicated to promoting Dynamic Data-Driven Application Systems (DDDAS) techniques and research. It accepts suggestions and contributions from all members of the DDDAS community. The site has hyperlinks to workshop reports, virtual proceedings, DDDAS projects, a bibliography (in BibTeX), and announcements or news.

The web site was created in 2001 after the 2000 workshop on DDDAS at the United States National Science Foundation that Dr. Frederica Darema organized. She first introduced the term DDDAS and pioneered it as a new paradigm. She later introduced the term InfoSymbiotic Systems.

For more than a decade, this web site carried the following text to describe DDDAS:

DDDAS is a paradigm whereby application (or simulations) and measurements become a symbiotic feedback control system. DDDAS entails the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement process. Such capabilities promise more accurate analysis and prediction, more precise controls, and more reliable outcomes. The ability of an application to control and guide the measurement process and determine when, where, and how it is best to gather additional data has itself the potential of enabling more effective measurement methodologies. Furthermore, the incorporation of dynamic inputs into an executing application invokes new system modalities and helps create application software systems that can more accur ately describe real world, complex systems. This enables the development of applications that intelligently adapt to evolving conditions and that infer new knowledge in ways that are not predetermined by the initialization parameters and initial static data.

Recently, researchers in the field have been discussing how the field might naturally incorporate Big Data techniques and communicate DDDAS concepts to Big Data threoreticians and practitioners.

Data assimilation is the scientific term used to describe the process of incorporating observations into a computer model of a real system. The weather forecasting community developed this paradigm first in the 1960's. Observations can come from many sources, but sensors can provide an almost infinite amount of data. Likewise, using software agents to mine very large, growing datasets or data streams can provide observations. Both are potentially Big Data sources that frequently are involved in data-driven applications using Big Computing environments.

A dynamic data-driven application system (DDDAS) is the integration of a simulation with dynamically assimilated data, multiscale modeling, computation, and a two way interaction between the simulation and the data acquisition methods. As an intellectual challenge, DDDAS research is about how to make the best use of the union of data acquisition, computation, and simulation methods in building systems. Creating a DDDAS requires understanding sensors, computing at levels ranging from an embedded processor to a supercomputer, networks, data security, mathematical and computational modeling, inverse problems, control theory, and visualization.

While data assimilation is concerned with better forecasts by integrating outcomes from “black box” models and new data, DDDAS redesigns the whole system by opening up the black box and allowing modification of the entire model and data collection processes. There are very interesting and different challenges in applied mathematics and computing for DDDAS over data assimilation as a result.


Dynamic applications have emerged in business, engineering and scientific processes, analysis, and design. The related field of Big Data has pushed into the financial sectors using similar techniques. Suggestions to customers, financial management, manufacturing process controls, resource management, weather and climate prediction, disaster management, traffic management, systems engineering, civil engineering, geological exploration, social and behavioral modeling, cognitive measurement, medical devices and surgical procedures, and bio-sensing are all examples where DDDAS has been used successfully.

DDDAS creates a rich set of new challenges for applications, algorithms, systems software, and measurement methods. DDDAS research typically requires strong, systematic collaborations between applications domain researchers and mathematics, statistics, and computer sciences researchers, as well as researchers involved in the design and implementation of measurement methods and instruments. Consequently, most DDDAS projects involve multidisciplinary teams of researchers.

An Example

Consider an autopilot for a commercial airplane. The airplane is told by a controller to go to one or more locations in a sequence. Using a variety of data, the vehicle goes there unless new locations are provided en route. Data acquisition includes airplane velocity, wind velocity, a three dimensional location with respect to the Earth, fuel loads, weather conditions en route, ice conditions on the airplane surface, fire conditions, engine data, and mechanical and sensor failure data. The autopilot maintains the flight route at altitudes that both dynamically change en route based on factors such as turbulence, flight lane rules, destination airport congestion, and changing weather.

Depending on flight conditions, nonlinear partial differential equations are solved using different scales to maintain the proper speed to get to the final destination at a specified time and with adequate remaining fuel. Data is regularly and securely transmitted to airline and airplane manufacturing computers and feedback data and subsequent queries are returned to the airplane.

Autopilots in modern airplanes have extensive capabilities well beyond anything possible in early ones, which only used data assimilation. Early airplanes used hydrolic systems to move flaps and rudders and the pilots flew the plane with the help of the autopilot. Modern airplanes use digital commands to move parts (i.e., fly by wire) so that pilots do not actually fly the airplane anymore. Instead, pilots give an airplane computer system commands that are executed only if they will work safely. The control systems of all modern airplanes are very complex DDDAS in action.

Similar Paradigms

Data assimilation has been an active research field since the 1960's and there remain many open questions independent of DDDAS, which places additional requirements on data assimilation.

CyberPhysical Systems (CPS) resembles the DDDAS paradigm. Researchers in the CPS field define it by, “CPS is about the intersection, not the union, of the physical and the cyber.” [http://cyberphysicalsystems.org] CPS is primarily an engineering field and they trace their roots back to the Norbert Weiner's work on automatic aiming and firing of antiaircraft guns in World War II. DDDAS differs since it is interested in the union, not the intersection, of the above.

The Internet of Things (IoT) is the network of physical objects accessed through the Internet. Each object has embedded processing so it can represent itself digitally. Hence, it can be controlled from anywhere. IoT means more data is gathered from more places with more ways to improve efficiency, safety, and security. Connecting all of these objects will lead to new automation capabilities in many fields that are not obviously related. The IoT represents subsets of either DDDAS or CPS.

Data Intensive Scientific Discovery (DISD) is sometimes promoted as the fourth paradigm in science. It represents how Big Data methods can be used to make scientific discoveries based on hidden data that has already been collected in databases. The dynamic aspect is through new data added to databases over time and data mining making discoveries. DISD represents only a small subset of either DDDAS or CPS.


The DDDAS.org web site has never used funds from any grant agency to support itself (the host has been provided by me at my own expense except when a substitute has been needed during hardware replacement periods). The host was located first at the University of Kentucky and since 2008 at the University of Wyoming.

The DDDAS field itself would not exist without the long term supprt provided first by the National Science Foundation (the Next Generation Software program, the DDDAS option in the Information Technology Research program, and the DDDAS program) and more recently by the Air Force Office of Scientific Research. Funding sources in Europe, Brasil, the Kingdom of Saudi Arabia, and possibly other countries have provided support for projects that have reported results on this web site. These agencies and their staffs are gratefully acknowledged by the DDDAS research community. Specific acknowledgments can be found on some of the web pages in this site.

Site Rebuild June 22-23, 2014

Due to the complete hardware failure of the roughly ten year old computer that hosted the web site, DDDAS.org was moved to a another computer. Should there be any interruptions, please contact me (see below) directly by email. Do not contact anyone else. Thank you for your patience and contributions.

Since I had to reload the web site, I updated all of the web pages to give them a cleaner and uniform appearance that works on computer screens, smartphones, and tablets. There should be no bad hyperlinks on the web site. If you find one, please notify me.

Craig C. Douglas

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