Workshop on Dynamic Data Computational Sciences
ICCS 2018, Wuxi, China
June 11-13, 2018
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, http://www.dddas.org). 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 2018, ICCS 2018, 11-13 June 2017, Wuxi, China, Yong Shi, Haohuan Fu, Yingjie Tian, Michael Lees, Valeria Krzhizhanovskaya, Jack Dongarra and Peter Sloot (eds.), Springer LNCS volumes 10861, New York, 2018. All papers for this workshop are in volume 10861. All papers for ICCS 2018 are in volumes 10860-10862.
Page numbers for individual papers are with each entry below.
- Baudouin Raoult, Giuseppe Di Fatta, Florian Pappenberger and Bryan Lawrence, Fast Retrieval of Weather Analogues in a Multi-petabytes Archive using Wavelet-based Fingerprints, pp. 697-710.
- Angel Farguell Caus, James Haley, Adam Kochanski, Jan Mandel and Ana Cortes Fite, Assimilation of satellite detections and fire perimeters by minimization of the residual in a fire spread model, pp. 711-723.
- Abani Patra, Analyzing Complex Models using Data and Statistics, pp. 724-736.
- Minghui Zhao, Lingling Zhang, Libin Zhang and Feng Wang, Research on Technology Foresight Method Based on Intelligent Convergence in Open Network Environment, pp. 737-74.
- Yunlan Wang, Jing Wang and Xingshe Zhou, Prediction of Blasting Vibration Intensity by Improved PSO-SVR on Apache Spark Cluste, pp. 748-75.
- Hassan Aboueisha, Victor Calo, Konrad Jopek, Mikhail Moshkov, Anna Paszynska and Maciej Paszynski, Bisections-weighted-by-element-size-and-order algorithm to optimize direct solver performance on 3D hp-adaptive grids, pp. 760-772.
- Robert Lodder, Mark Ensor and Cynthia Dickerson, Establishing EDI for a Clinical Trial of a Treatment for Chikungunya, pp. 772-782.
- Craig C. Douglas and Krishanthan Krishnamoorthy, Deadlock Detection in MPI Programs Using Static Analysis and Symbolic Execution, pp. 783-796.