Data through the Computational Lens

Today, many advanced studies in computational science are enforced by data collected and processed in distributed systems, obtained and assimilated in real (or near-real) time, used to identify and build models, or even used for advanced visualization to have insights through visual images. Taking such an important role of data into account, 16th International Conference on Computational Science (ICCS 2016), an event that promotes leading-edge research in the area, proposed the special theme “Data through the Computational Lens” to bring diverse ideas and recent developments together from computational science society aimed towards this vector. This special issue contains extended papers selected from the conference proceedings that promotes leading edge research.

[1]  Jack J. Dongarra,et al.  Fast Cholesky factorization on GPUs for batch and native modes in MAGMA , 2017, J. Comput. Sci..

[2]  Michal Owsiak,et al.  Running simultaneous Kepler sessions for the parallelization of parametric scans and optimization studies applied to complex workflows , 2017, J. Comput. Sci..

[3]  Giovanni Samaey,et al.  Variance-reduced multiscale simulation of slow-fast stochastic differential equations , 2017, J. Comput. Sci..

[4]  Peter M. A. Sloot,et al.  Data through the Computational Lens, Preface for ICCS 2016 , 2016, ICCS.

[5]  Alexander Boukhanovsky,et al.  A multi-layer model for diffusion of urgent information in mobile networks , 2017, J. Comput. Sci..

[6]  David Abramson,et al.  Perspectives of the International Conference of Computational Science 2014 , 2015, J. Comput. Sci..

[7]  Peter M. A. Sloot.  More likely we would be ritually slaughtered , 2017 .

[8]  Marek Kisiel-Dorohinicki,et al.  Buffered local search for efficient memetic agent-based continuous optimization , 2017, J. Comput. Sci..

[9]  George Kampis,et al.  Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory , 2017, ICCS.

[10]  T. VaisaghViswanathan,et al.  Influence of charging behaviour given charging infrastructure specification: A case study of Singapore , 2017, J. Comput. Sci..

[11]  Yao Fu,et al.  Bridging the multi phase-field and molecular dynamics models for the solidification of nano-crystals , 2016, J. Comput. Sci..

[12]  Jari Toivanen,et al.  Reduced order models for pricing European and American options under stochastic volatility and jump-diffusion models , 2016, J. Comput. Sci..

[13]  Craig C. Douglas,et al.  A spectral projection preconditioner for solving ill conditioned linear systems , 2016, J. Comput. Sci..

[14]  Yunsong Wang,et al.  Competing energy lookup algorithms in Monte Carlo neutron transport calculations and their optimization on CPU and Intel MIC architectures , 2017, J. Comput. Sci..

[15]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[16]  Valeria V. Krzhizhanovskaya,et al.  Anomaly detection in earth dam and levee passive seismic data using support vector machines and automatic feature selection , 2017, J. Comput. Sci..

[17]  Ilkay Altintas,et al.  Biomedical Big Data Training Collaborative (BBDTC): An effort to bridge the talent gap in biomedical science and research , 2017, J. Comput. Sci..

[18]  B. Shneiderman.  Science 2.0 , 2008, Science.

[19]  Manuel Graña,et al.  Using Anticipative Hybrid Extreme Rotation Forest to predict emergency service readmission risk , 2017, J. Comput. Sci..