SC15 Austin, TX

Scaling Smart Appliances for Spatial Data Synthesis


Authors: Luis Pineda-Morales (Microsoft Corporation and French Institute for Research in Computer Science and Automation), Balaji Subramaniam (Argonne National Laboratory), Kate Keahey (Argonne National Laboratory), Gabriel Antoniu (French Institute for Research in Computer Science and Automation), Alexandru Costan (French Institute for Research in Computer Science and Automation), Shaowen Wang (University of Illinois at Urbana-Champaign), Anand Padmanabhan (University of Illinois at Urbana-Champaign), Aiman Soliman (University of Illinois at Urbana-Champaign)

Abstract: Several scientific domains rely on the ability to synthesize spatial data, embedded with geographic references. Economics and sociology, for instance, use spatial data to analyze and describe population dynamics. As the sources of spatial data, such as sensors and social media, have become more accurate and numerous, the generated data has considerably grown in size and complexity over the past years. As a consequence, larger computing capabilities are required for storing, processing and visualizing the data. In the recent years, cloud computing has emerged as a convenient infrastructure for supporting current spatial data synthesis needs, since they offer dynamically provisioned and fairly inexpensive resources. In this poster, we aim to leverage cloud computing resources for enabling spatial data synthesis.

Poster: pdf
Two-page extended abstract: pdf


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