DescriptionSpatiotemporal data, whether captured through remote sensors, ground and ocean sensors, social media and handhelds, traffic-related sensors and cameras, medical imaging, or large scale simulations have always been “big.” A common thread among all these big collections of datasets sets is that they are spatial and temporal. Processing and analyzing these datasets requires high-performance computing infrastructures. Despite these commonalities, leading big data communities of bio, geo, climate and social sciences, are highly fragmented and work in silos, resulting in solutions that are difficult to discover, integrate, and cross-fertilize. This panel aims to bring together the aforementioned, diverse yet overlapping communities with substantive big data and compute problems under SC umbrella to facilitate dialogue to reduce the impedances.
- HPC and Spatial-temporal Computing - two ships passing in the night? - Does HPC offer a mechanism to facilitate cross-fertilization? - Impedances to large-scale adoption of Spatial Computation and Analytics?