Workshop on Dynamic Data-Driven Applications Systems / ICCS 2014, Cairns, Australia

June 10-12, 2014


This workshop covers several aspects of the Dynamic Data Driven Applications Systems (DDDAS) concept, which is an established approach defining a symbiotic relation between an application and sensor based measurement systems. Applications can accept and respond dynamically to new data injected into the executing application. In addition, applications can dynamically control the measurement processes. The synergistic feedback control-loop between an application simulation and its measurements opens new capabilities in simulations, e.g., the creation of applications with new and enhanced analysis and prediction capabilities, greater accuracy, longer simulations between restarts, and enable a new methodology for more efficient and effective measurements. DDDAS transforms the way science and engineering are done with a major impact in the way many functions in our society are conducted, e.g., manufacturing, commerce, transportation, hazard prediction and management, and medicine. The workshop will present such new opportunities as well as the challenges and approaches in technology needed to enable DDDAS capabilities in applications, relevant algorithms, and software systems. The workshop will showcase ongoing research in these aspects with examples from several important application areas. All related areas in Data-Driven Sciences are included in this workshop.

This workhop was part of the International Conference on Computational Sciences 2014.


Craig C. Douglas, University of Wyoming

Abani Patra, University of Buffalo

Ana Cortés, Universitat Autonoma de Barcelona

Papers and citations

The citation is Procedia Computer Science, 29 (2014), pp. 1-2520. The volume is online at Procedia Computer Science, vol. 29, pp. 1-2520.

Page numbers for individual papers are with each entry below. The speaker’s name is underlined.

Tuesday, June 10

Session I

  • Robert McCune and Greg Madey, Control of Artificial Swarms with DDDAS, pp. 1171-1181.
  • Doug Allaire, David Kordonowy, Marc Lecerf, Laura Mainini and Karen Willcox, Multifidelity DDDAS Methods with Application to a Self-Aware Aerospace Vehicle, pp. 1182-1192.
  • Kishan Sudusinghe, Inkeun Cho, Mihaela van der Schaar and Shuvra Bhattacharyya, Model Based Design Environment for Data-Driven Embedded Signal Processing Systems, pp. 1193-1202.
  • Richard Fujimoto, Angshuman Guin, Michael Hunter, Haesun Park, Ramakrishnan Kannan, Gaurav Kanitkar, Michael Milholen, Sabra Neal and Philip Pecher, A Dynamic Data Driven Application System for Vehicle Tracking, pp. 1203-1215.

Session II

  • Tomas Artes, Andres Cencerrado, Ana Cortes, Tomas Margalef, Dario Rodr-guez, Thomas Petroliagkis and Jesus San Miguel, Towards a Dynamic Data Driven Wildfire Behavior Prediction System at European Level, pp. 1216-1226.
  • A. K. Patra, E. R. Stefanescu, R. M. Madankan, M. I Bursik, E. B. Pitman, P. Singla, T. Singh and P. Webley, Fast Construction of Surrogates for UQ Central to DDDAS – Application to Volcanic Ash Transport, pp. 1227-1235.
  • Md. Sumon Shahriar and John McCulloch, A Dynamic Data-driven Decision Support for Aquaculture Farm Closure, pp. 1236-1245.
  • Craig C. Douglas, An Open Framework for Dynamic Big-Data-Driven Application Systems (DBDDAS) Development, pp. 1246-1255.

Wednesday, June 11

Session III

  • Vishwas Hebbur Venkata Subba Rao and Adrian Sandu, A posteriori Error Estimates for DDDAS inference problems, pp. 1256-1265.
  • Piyush Tagade, Hansjorg Seybold and Sai Ravela, Mixture Ensembles for Data Assimilation in Dynamic Data-Driven Environmental Systems, pp. 1266-1276.
  • Haitao Wei, Xiaoming Li, Guang Gao and Stephane Zuckerman, A Dataflow Programming Language and Its Compiler for Streaming Systems, pp. 1289-1298.
  • Lucas Krakow, Louis Rabiet, Yun Zou, Guillaume Iooss, Edwin Chong and Sanjay Rajopadhye, Optimizing Dynamic Resource Allocation, pp. 1277-1288.
  • Erik Blasch, Youssif Al-Nashif and Salim Hariri, Static versus Dynamic Data Information Fusion analysis using DDDAS for Cyber Trust, pp. 1299-1313.

Session IV

  • Layla Pournajaf, Li Xiong and Vaidy Sunderam, Dynamic Data Driven Crowd Sensing Task Assignment, pp. 1314-1323.
  • Shashi Phoha, Nurali Virani, Pritthi Chattopadhyay, Soumalya Sarkar, Brian Smith and Asok Ray, Context-aware Dynamic Data Driven Pattern Classification for Multi-Layered Border Control Sensor-net, pp. 1324-1333.
  • Ana Cortes, Craig C. Douglas, and Abani Patra, DDDAS Discussion.