Scalable Retrieval and Analysis of Simulation and Observation Data Ensembles
K. Selçuk Candan
Professor, School of Computing, Informatics, and Decision Systems Engineering, ASU
Friday, January 30, 2015
Brickyard (BYENG) 150, Tempe campus [map]
Data- and model-driven computer simulations for understanding spatio-temporal dynamics of emerging phenomena are increasingly critical in various application domains, from predicting geo-temporal evolution of epidemics to helping reduce energy footprints of buildings leading to more sustainable building systems and architectural designs. These simulations track 10s or 100s of inter-dependent parameters, spanning multiple information layers and spatio-temporal frames, affected by complex dynamic processes operating at different resolutions. Consequently, the key characteristics of data sets and models relevant to these data-intensive simulations often include the following: (a) voluminous, (b) multi-variate, (c) multi-resolution, (d) spatio-temporal, and (e) inter-dependent. While very powerful and highly modular and flexible simulation software exists, because of the volume and complexity of the simulation data, the varying spatial and temporal scales at which the key transmission processes operate and relevant observations are made, today experts lack the means to adequately and systematically interpret observations, understand the underlying processes, and re-use of existing simulation results in new settings. In this talk, I will introduce computational challenges that arise from the need to process, index, search, and analyze, in a scalable manner, large volumes of temporal data resulting from data-intensive simulations and present some solutions.
Feb. 6 – Violet Syrotiuk, CIDSE
Feb. 13 – Xin Wei Sha, AME