Dynamic Data-Driven Application Systems



Dynamic Data-Driven Applications

DDDAS.org is a scientific community web site dedicated since 2001 to promoting Dynamic Data-Driven Application Systems (DDDAS) techniques and research. It accepts suggestions and contributions for the web site 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.

A dynamic data-driven application system is the integration of a simulation with dynamically assimilated data, multiscale modeling, computation, and a two way interaction between the model execution and the data acquisition methods.

Observations can come from many sources, but sensors can provide an almost infinite amount of data. Using software agents to mine very large, growing datasets or data streams can also provide observations. Both are potentially Big Data [Wikipedia] sources that frequently are involved in data-driven applications using Big Computing environments. Researchers in the DDDAS field have been discussing recently how to naturally incorporate Big Data techniques and communicate DDDAS concepts to Big Data theoreticians and practitioners.

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.

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.


Suggestions to online 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.

An Example

A tranformative way to land airplanes on time and reduce delays and cancellations is a process known as Time Based Flow Systems (TBFS) [UK NATS]. It spaces planes by space instead of by time. The first of these systems was developed for Heathrow Airport by Lockheed Martin for the British National Air Traffic Services (NATS) and fully deployed in May, 2015. It has reduced flight cancellations due to wind by exactly 100% and flight delays by approximately 40% during the period of May - August, 2015.

Traditional flight landing systems space planes 3 - 7 miles apart on final approach with an average of 5 miles separation can be replaced with a TBFS system that dynamically reduces the separation based on a safe time interval that corresponds to as little as 3 miles and still ensure that all landings are safe.

Airlines and air traffic controllers have long viewed time based separation as the most reliable air transport system. Congested airports all over the world will benefit. They schedule flights for perfect weather conditions, but the slightest anomaly can create chaos.

There is no similar system in place anywhere in the world today. The United States FAA will issue a report on December 15, 2015 with its views. The U.S. Air Force Office of Scientific Research has nothing in the works even though it has a DDDAS program that should be on the forefront of this type of research and deployment nationally.

Similar Paradigms or Ones of Interest to DDDAS

Data assimilation is the scientific term used to describe “the process of incorporating observations into a computer model of a real system.” [Wikipedia] Researchers using satellite data in the late 1960's initially developed this transformative research field. Space scientists at NASA labs and weather scientists at the National Center for Atmostpheric Research (NCAR) were the primary initial researchers.

Data assimilation has been an active research field and there remain many open questions independent of DDDAS (which places additional requirements on data assimilation). 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.

CyberPhysical Systems (CPS) resembles the DDDAS paradigm, but is different. 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 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. An example CPS is an embedded system in a shoe that transmits the wearer's activity data to an analysis application running on a nearby mobile phone. Probably the most visible CPS is Apple's Health app and HealthKit library that was introduced with iOS 8 for iPhones and iPads.

The Internet of Things (IoT) is the network of physical objects accessed through the Internet [WhatIs]. 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. An IoT example is a thermostat for a heating or cooling system that is connected to the Internet and can be remotely controlled.

Data Intensive Scientific Discovery (DISD) is sometimes promoted as the fourth paradigm in science [Microsoft Research]. 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. An example of DISD is searching through all available medical databases, which grows over time, and during a search finding the origin of a disease.


The DDDAS.org web site is self supported without aid from government grants. The host was located from 2001-2008 at the University of Kentucky and since 2008 at the University of Wyoming. Over the years many people from many institutions have contributed information and time to this web site. Their contributions are gratefully acknowledged.

The DDDAS field itself would not exist in its present state without the long term, generous support provided by the National Science Foundation, the Air Force Office of Scientific Research, and funding sources in Europe, Brasil, the Kingdom of Saudi Arabia, and possibly other countries. The DDDAS research community gratefully acknowledges these funding sources and their staffs. Specific acknowledgments can be found on some web pages in this site.

This website has been established and is maintained by Prof. Craig C. Douglas.
©2014-2020 by www.dddas.org and Craig C. Douglas