Emerging trends in monitoring landscapes and energy infrastructures with big spatial data

Bhaduri, Budhendra; Patlolla, Dilip R.; Vatsavai, Ranga Raju; Cheriyadat, Anil M.; Lü, Wei; Karthik, Rajasekar

doi:10.1145/2766196.2766202

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Geographic Information Science and Technology (GIST) and The Urban Dynamics Institute (UDI) at Oak Ridge National Laboratory (ORNL) are building such a CI platform by integrating our next-generation EO-HPC systems under one umbrella using novel techniques and emerging technologies. We employ wide breadth of techniques across the spectrum of scientific computing such as GPGPU, distributed, real-time and cluster computing (Bhaduri et al 2015a;Karthik 2014a;Sorokine et al 2012). The platform is designed to scale to petabytes of data and handle massive workloads.…”

Section: Introductionmentioning

confidence: 99%

Earth Observation Open Science and Innovation

Mathieu¹,

Aubrecht²

2018

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use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Introductionmentioning

confidence: 99%

Earth Observation Open Science and Innovation

Mathieu¹,

Aubrecht²

2018

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“…Bringing these systems together to create a fully and closely knitted Cyber-infrastructure (CI) platform provides a shared universe for EO data driven analysis and discovery capabilities for US federal agencies. Though there have been some promising community efforts to build a CI platform that can integrate various types of EO-HPC systems together, there does not exist a unified CI platform currently (Kalidindi 2015;Bhaduri et al 2015a). With "Big Data" explosion changing the landscape of software architecture design, there is an increasing need for such a platform that can meet the modern complex needs.…”

Section: Introductionmentioning

confidence: 99%

“…We employ wide breadth of techniques across the spectrum of scientific computing such as GPGPU, distributed, real-time and cluster computing (Bhaduri et al 2015a;Karthik 2014a;Sorokine et al 2012). The platform is designed to scale to petabytes of data and handle massive workloads.…”

Section: Introductionmentioning

confidence: 99%

Cyber-Infrastructure for Data-Intensive Geospatial Computing

Karthik

Sorokine

Patlolla

et al. 2018

Earth Observation Open Science and Innovation

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With the recent advent of heterogeneous High-performance Computing (HPC) to handle EO "Big Data" workloads, there is a need for a unified Cyber-infrastructure (CI) platform that can bridge the best of many HPC worlds. In this chapter, we discuss such a CI platform being developed at Geographic Information Science and Technology (GIST) group using novel and innovative techniques, and emerging technologies that are scalable to large-scale supercomputers. The CI platform utilizes a wide variety of computing such as GPGPU, distributed, real-time and cluster computing, which are being brought together architecturally to enable datadriven analysis, scientific understanding of earth system models, and research collaboration. This development addresses the need for close integration of EO and other geospatial information in the face of growing volumes of the data, and facilitates spatio-temporal analysis of disparate and dynamic data streams. Horizontal scalability and linear throughput are supported in the heart of the platform itself. It is being used to support very broad application areas, ranging from high-resolution settlement mapping, national bioenergy infrastructure to urban information and mobility systems. The platform provides spatio-temporal decision support capabilities in planning, policy and operational missions for US federal agencies. Also, the platform is designed to be functionally and technologically sustainable for continued support of the US energy and environment mission for the coming decades.

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