Workshop on Dynamic Data Computational Sciences
ICCS 2019, Faro, Portugal
June 12-14, 2019
Abstract
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 Computational Science – ICCS 2019, 19th International Conference Faro, Portugal, June 12–14, 2019, Proceedings, Part IV, João M. F. Rodrigues, Pedro J. S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Jack J. Dongarra, and Peter M.A. Sloot (eds.), Springer LNCS volume 11539, Cam, Switzerland, 2019. All papers for ICCS 2019 are in Springer LNCS volumes 11536–11540.
Page numbers for individual papers in LNCS 11539 are with each entry below.
Presentations/Papers
- Anne D. Brooks and Robert A. Lodder, Nonparametric Approach to Weak Signal Detection in the Search for Extraterrestrial Intelligence (SETI), pp. 3–15.
- Junteng Hou, Shupeng Wang, Guangjun Wu, Ge Fu, Siyu Jia, Yong Wang, and Binbin Li, Parallel Strongly Connected Components Detection with Multi-partition on GPUs, pp. 16–30.
- Michel Pires, Nicollas Silva, Leonardo Rocha, Wagner Meira, and Renato Ferreira, Efficient Parallel Associative Classification Based on Rules Memoization, pp. 31–44.
- Sreelekha Guggilam, Syed Mohammed Arshad Zaidi, Varun Chandola, and Abani K. Patra, Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data, pp. 45–59.
- Xiukun Hu and Craig C. Douglas, An Implementation of a Coupled Dual-Porosity-Stokes Model with FeniCS, pp. 60–73.
- Shamoz Shah and Madhu Goyal, Anomaly Detection in Social Media Using Recurrent Neural Network, pp. 74–83.
- Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han, and Songlin Hu, Conditional BERT Contextual Augmentation, pp. 84–95.
- Ricardo Martins, Alberto Azevedo, André B. Fortunato, Elsa Alves, Anabela Oliveira, and Alexandra Carvalho, An Innovative and Reliable Water Leak Detection Service Supported by Data-Intensive Remote Sensing Processing, pp. 96–108.