Introduction to the ICCS2006 Workshop on Dynamic Data Driven Applications Systems

  • Frederica Darema
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


The Dynamic Data Driven Application Systems (DDDAS) concept entails the ability to incorporate dynamically data into an executing application simulation, and in reverse, the ability of applications to dynamically steer measurement processes. Such dynamic data inputs can be acquired in real-time on-line or they can be archival data. DDDAS offers the promise of improving modeling methods, augmenting the analysis and prediction capabilities of application simulations, improving the efficiency of simulations and the effectiveness of measurement systems.

The scope of the present workshop provides examples of research and technology advances in enabling the DDDAS capabilities.


Water Distribution System Application Simulation Data Assimilation Technique Emergency Response System Information Technology Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    NSF Workshop (March 2000),
  2. 2.
    Darema, F.: Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements. In: ICCS 2004 (2004)Google Scholar
  3. 3.
    Darema, F.: Grid Computing and Beyond: The Context of Dynamic Data Driven Applications Systems. Proceedings of the IEEE, Special Issue on Grid Computing (March 2005)Google Scholar
  4. 4.
    Darema, F.: Dynamic Data Driven Applications Systems: New Capabilities for Application Simulations and Measurements. In: ICCS 2005 (2005)Google Scholar
  5. 5.
    DDDAS-Dynamic Data Driven Applications Systems Program Solicitation (NSF 05-570),
  6. 6.
    NSF Information Technology Research (ITR) Program (1999-2004)Google Scholar
  7. 7.
  8. 8.
    NSF Sponsored Workshop: DDDAS-Dynamic Data Driven Applications Systems (January 19-20, 2006),
  9. 9.
    Parashar, M., Matossian, V., Klie, H., Thomas, S.G., Wheeler, M.F., Kurc, T., Saltz, J., Versteg, R.: Towards Dynamic Data-Driven Management of the Ruby Gulch Waste RepositoryGoogle Scholar
  10. 10.
    Douglas, C.C., Clay Harris, J., Iskandarani, M., Johnson, C.R., Lodder, R., Parker, S., Cole, M.J., Ewing, R., Efendiev, Y., Lazarov, R., Qin, G.: Dynamic Contaminant Identification in WaterGoogle Scholar
  11. 11.
    Mahinthakumar, K., von Laszewski, G., Ranjithan, R., Brill, D., Uber, J., Harrison, K., Sreepathi, S., Zechman, E.: An Adaptive Cyberinfrastructure for Threat Management in Urban Water Distribution SystemsGoogle Scholar
  12. 12.
    Flikkema, P.G., Agarwal, P.K., Clark, J.S., Ellis, C., Gelfand, A., Munagala, K., Yang, J.: Model-Driven Dynamic Control of Embedded Wireless Sensor NetworksGoogle Scholar
  13. 13.
    Madey, G.R., Szabo, G., Barabási, A.-L.: WIPER: The Integrated Wireless Phone Based Emergency Response SystemGoogle Scholar
  14. 14.
    Fujimoto, R.M., Guensler, R., Hunter, M., Kim, H.K., Lee, J., Leonard II, J., Palekar, M., Schwan, K., Seshasayee, B.: Dynamic Data Driven Application Simulation of Surface Transportation SystemsGoogle Scholar
  15. 15.
    Chaturvedi, A., Mellema, A., Filatyev, S., Gore, J.: DDDAS Approach to Fire and Agent Evacuation Modeling: Case Study of Rhode Island Nightclub FireGoogle Scholar
  16. 16.
    McCalley, J.D., Honavar, V.G., Ryan, S.M., Meeker, W.Q., Roberts, R.A., Qiao, D., Li, Y.: Auto- Steered Information-Decision Processes for Electric System Asset ManagementGoogle Scholar
  17. 17.
    Abed, E.H., Nmachchivaya, N.S., Overbye, T.J., Pai, M.A., Sauer, P.W., Sussman, A.: Data-Driven Power System OperationsGoogle Scholar
  18. 18.
    Farhat, C., Michopoulos, J.G., Chang, F.K., Guibas, L.J., Lew, A.J.: Towards a Dynamic Data Driven System for Structural and Material Health MonitoringGoogle Scholar
  19. 19.
    Awan, A., Sameh, A., Grama, A.: The Omni Macroprogramming Environment for Sensor NetworksGoogle Scholar
  20. 20.
    Knight, D., Rossman, T., Jaluria, Y.: Evaluation of Fluid-Thermal Systems by Dynamic Data Driven Application SystemsGoogle Scholar
  21. 21.
    Akcelik, V., Biros, G., Draganescu, A., Ghattas, O., Hill, J., van BloemenWaanders, B.: Inversion of Airborne Contaminants in a Regional ModelGoogle Scholar
  22. 22.
    Kim, S., Chandrasekar, J., Ridley, A., Bernstein, D.S.: Data Assimilation Using the Global Ionosphere-Thermosphere ModelGoogle Scholar
  23. 23.
    Ravela, S.: Amplitude-Position formulation of Data AssimilationGoogle Scholar
  24. 24.
    Son, H.-J., Trafalis, T.B.: Detection of Tornados Using an Incremental Revised Support Vector Machine with FiltersGoogle Scholar
  25. 25.
    Golubchik, L., Caron, D., Das, A., Dhariwal, A., Govindan, R., Kempe, D., Oberg, C., Sharma, A., Stauer, B., Sukhatme, G., Zhang, B.: A Generic Multi-scale Modeling Framework for Reactive Observing Systems: an OverviewGoogle Scholar
  26. 26.
    Douglas, C.C., Beezley, J.D., Coen, J., Li, D., Li, W., Mandel, A.K., Mandel, J., Qin, G., Vodacek, A.: Demonstrating the Validity of a Wildfire DDDASGoogle Scholar
  27. 27.
    Oden, J.T., Diller, K.R., Bajaj, C., Browne, J.C., Hazle2, J., Babuska, I., Bass, J., Demkowicz, L., Feng, Y., Fuentes, D., Prudhomme, S., Rylander, N., Sta_ord, R.J., Zhang, Y.: Development of a Computational Paradigm for Laser Treatment of CancerGoogle Scholar
  28. 28.
    Richardson, P.D., Pivkin, I.V., Karniadakis, G.E., Laidlaw, D.H.: Blood Flow At Arterial Branches: Complexities To Resolve For The Angioplasty SuiteGoogle Scholar
  29. 29.
    Fortes, J., Figueiredo, R., Hermer-Vazquez, L., Príncipe, J., Sanchez, J.C.: A New Architecture for Deriving Dynamic Brain-Machine InterfacesGoogle Scholar
  30. 30.
    Metaxas, D., Tsechpenakis, G., Li, Z., Huang, Y., Kanaujia, A.: Dynamically Adaptive Tracking of Gestures and Facial ExpressionsGoogle Scholar
  31. 31.
    Kennedy, C., Theodoropoulos, G.: Intelligent Management of Data Driven Simulations to Support Model building in the Social sciencesGoogle Scholar
  32. 32.
    Reynolds, P., Brogan, D., Carnahan, J., Loitiére, Y., Spiegel, M.: Capturing Scientists’ Insight for DDDASGoogle Scholar
  33. 33.
    Rahmani, A.T., Rafe, V., Sedighian, S., Abbaspour, A.: An MDA-based Modeling and Design of Service-Oriented ArchitectureGoogle Scholar
  34. 34.
    Jones, A., Cornford, D.: Advanced Data Driven Visualisation for Geo-spatial DataGoogle Scholar
  35. 35.
    Bo, L., Jun, Z., Jixin, Q.: Design and Analysis of Test Signals for System IdentificationGoogle Scholar
  36. 36.
    Gao, X., Fan, Z.: The Research on the Method of Process-based Knowledge catalog & Storage and its Application in Steel Product R&DGoogle Scholar
  37. 37.
    Constantinescu, E.M., Sandu, A., Carmichael, G.R., Chai, T., Seinfeld, J.H., D¢aescu, D.: Localized Ensemble Kalman Data Assimilation for Atmospheric Chemical Transport ModelsGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Frederica Darema
    • 1
  1. 1.National Science FoundationArlingtonUSA

Personalised recommendations