Workshop on Dynamic Data Computational Sciences / ICCS 2017, Zurich, Switzerland

Workshop on Dynamic Data Computational Sciences
ICCS 2017, Zurich, Switzerland

June 12-14, 2017

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 International Conference on Computational Science 2017, ICCS 2017, 12-14 June 2017, Zurich, Switzerland, Petros Koumoutsakos, Michael Lees, Valeria Krzhizhanovskaya, Jack Dongarra and Peter Sloot (eds.), Procedia Computer Science, 108 (2017), pp. 1-2546. The volume is online at Procedia Computer Science, vol. 108, pp. 1-2546.

Page numbers for individual papers are with each entry below.

Presentations/Papers

  • E. Bruce Pitman, Abani K. Patra, and Keith Dalbey, Fast Construction an Emulators via Localization.
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  • Oriol Rios, M. Miguel Valero, Elsa Pastor, and Eulalia Planas, Optimization strategy exploration in a wildfire propagation data driven system.
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  • À. Farguell, A. Cortés, T. Margalef, J.R. Miro, and J. Mercader, Data resolution effects on a coupled data driven system for forest fire propagation prediction, pp. 1562-1571.
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  • Thayjes Srivas, Raymond A. de Callafon, Daniel Crawl, and Ilkay Altintas, Data Assimilation of Wildfires with Fuel Adjustment Factors in farsite using Ensemble Kalman Filtering, pp. 1572-1581.
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  • Sai Akhi,l R. Konakalla, and Raymond A. de Callafon, Feature Based Grid Event Classification from Synchrophasor Data, pp. 1582-1591.
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  • Weiner Oliveira, Lenitta M. Ambrósio, Regina Braga, Victor Ströele, José Maria David, and Fernanda Campos, A Framework for Provenance Analysis and Visualization, pp. 1592-1601.
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  • Craig C. Douglas and Robert A. Lodder, Human Identification and Localization by Robots in Collaborative Environments, pp. 1602-1611.
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  • Robert A. Lodder, Data-Driven Design of an Ebola Therapeutic, pp. 1612-1621.
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  • Marco Strutz, Hermann Heßling, and Achim Streit, Transforming a Local Medical Image Analysis for Running on a Hadoop Cluster, pp.1622-1631 .
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  • Tobias Ritter, Stefan Ulbrich, and Oskar von Stryk, Decentralized Dynamic Data-Driven Monitoring of Dispersion Processes on Partitioned Domains, pp. 1632-1641.
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  • Gabriel Ramos, Guilherme Andrade, Rafael Sachetto, Daniel Madeira, Renan Carvalho, Renato Ferreira, Fernando Mourão, and Leonardo Rocha, A Framework for Direct and Transparent Data Exchange of Filter-stream Applications in Multi-GPUs Architectures, pp. 1642-1651.
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  • Prashant Shekhar, Abani Patra, and Beata M. Csatho, Multiscale and Multiresolution methods for Sparse representation of Large datasets, pp. 1652-1661.
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  • Kazuhisa Chiba and Masaya Nakata, From Extraction to Generation of Design Information -Paradigm Shift in Data Mining via Evolutionary Learning Classifier System, pp. 1662-1671.
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  • Sukanta Roy, Sanchit Gupta, and S.N. Omkar, Case study on: Scalability of preprocessing procedure of remote sensing in Hadoop, pp. 1672-1681.
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  • Kai Zhang, Chao Li, Yong Wang, Xiaobin Zhu, and Haiping Wang, Collaborative Support Vector Machine for Malware Detection, pp. 1682-1691.
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  • Daniel Silva-Palacios, Cèsar Ferri, and María José Ramírez-Quintana, Improving Performance of Multiclass Classification by Inducing Class Hierarchies, pp. 1692-1701.
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  • Hamidreza Anvari andPaul Lu, The Impact of Large-Data Transfers in Shared Wide-Area Networks: An Empirical Study, pp.1702-1711 .
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  • C. Brun, T. Artes, A. Cencerrado, T. Margalef, and A. Cortés, A High Performance Computing Framework for Continental-Scale Forest Fire Spread Prediction, pp. 1712-1721.
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