Workshop on Dynamic Data Computational Sciences/ ICCS 2016, San Diego, California, United States of America

Workshop on Dynamic Data Computational Sciences
ICCS 2016, San Diego, California, United States of America

June 6-8, 2016


In the late 1960’s, simple data assimilation revolutionarily transformed science in fields based on satellite data. Both NASA and NCAR produced stunningly revolutionary applications. The oil and gas industry jumped on this concept in the early to mid 1970’s creating commercial data assimilation pipeline products by multiple vendors that were used in more than 165 countries in short order. This led to intelligent data assimilation being the normal way to operate a reservoir or pipeline networks by the 1990’s by all of the major oil producers. Since the early 2000’s, government grant agencies (e.g., the National Science Foundation) applied this concept to update numerous fields creating astonishing improvemnts in simulations that continue to this day in many application areas.A data-driven computational system is the integration of a simulation with dynamically and intelligently assimilated data, multiscale modeling, computation, and a two way interaction between the model execution and the data acquisition methods (see the DDDAS Scientific Community Web Site, The workshop will present opportunities as well as challenges and approaches in technology needed to enable Data-Driven Computational Science capabilities in applications, relevant algorithms, and software systems. All related areas in Data-Driven Sciences are included in this workshop, including CyberPhysical Systems like HealthKit on iPhones and iPads as well as similar systems developed by Intel, Google, and Microsoft for phones and tablets, Internet of Things (IoT), Cloud of Things (CoT), and Data Intensive Scientific Discovery (DISD).A recent example is a tranformative way of landing airplanes on time and reduce delays and cancellations is a process known as Time Based Flow Systems (TBFS). 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 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.

Papers and citations

The citation is International Conference on Computational Science 2016, ICCS 2016, 6-8 June 2016, San Diego, California, USA, Ilkay Altintas, Michael Norman, Jack Dongarra, ValeriaV. Krzhizhanovskaya, Michael Lees and Peter M.A. Sloot (eds.), Procedia Computer Science, 80 (2016), pp. 1-2464. The volume is online at Procedia Computer Science, vol. 80, pp. 1-2464.

Page numbers for individual papers are with each entry below. For the third year since 2013, one of the ICCS best papers was awarded to a paper from our workshop.


  • Mo Mu, Decoupling Techniques for Coupled PDE Models in Multi-Physics Applications: Algorithm, Analysis, and Software.
  • Prashant Shekhar, Abani Patra, and E.R. Stefanescu, Multilevel Methods for Sparse Representation of Topographical Data, pp. 887-896.
  • Thayjes Srivas, Tomàs Artés, Raymond A. de Callafon, and Ilkay Altintas, Wildfire Spread Prediction and Assimilation for FARSITE Using Ensemble Kalman Filtering, pp. 897-908.
  • Tomàs Artés, Ana Cortés, and Tomàs Margalef, Large Forest Fire Spread Prediction: Data and Computational Science, pp. 909-918.
  • Tobias Ritter, Juliane Euler, Stefan Ulbrich, and Oskar von Stryk, Decentralized Dynamic Data-driven Monitoring of Atmospheric Dispersion ProcessesOriginal Research Article, pp. 919-930.
  • Sai Akhil R. Konakalla, and Raymond de Callafon,Optimal Filtering for Grid Event Detection from Real-time Synchrophasor Data, pp. 931-940.
  • Craig C. Douglas, Long Lee, and Man-Chung Yeung, On Solving Ill Conditioned Linear Systems, pp. 941-950. Winner of an ICCS 2016 Best Paper.
  • Danilo Melo, Sávyo Toledo, Fernando Mourão, Rafael Sachetto, Guilherme Andrade, Renato Ferreira, Srinivasan Parthasarathy, and Leonardo Rocha, Hierarchical Density-Based Clustering Based on GPU Accelerated Data Indexing Strategy, pp. 951-961.