PlanetSense: A Real-time Streaming and Spatio-temporal Analytics Platform for Gathering Geo-spatial Intelligence from Open Source Data
Abstract
Geospatial intelligence has traditionally relied on the use of archived and unvarying data for planning and exploration purposes. In consequence, the tools and methods that are architected to provide insight and generate projections only rely on such datasets. Albeit, if this approach has proven effective in several cases, such as land use identification and route mapping, it has severely restricted the ability of researchers to inculcate current information in their work. This approach is inadequate in scenarios requiring real-time information to act and to adjust in ever changing dynamic environments, such as evacuation and rescue missions. In this work, we propose PlanetSense, a platform for geospatial intelligence that is built to harness the existing power of archived data and add to that, the dynamics of real-time streams, seamlessly integrated with sophisticated data mining algorithms and analytics tools for generating operational intelligence on the fly. The platform has four main components – i) GeoData Cloud – a data architecture for storing and managing disparate datasets; ii) Mechanism to harvest real-time streaming data; iii) Data analytics framework; iv) Presentation and visualization through web interface and RESTful services. Using two case studies, we underpin the necessity of our platform in modeling ambient populationmore »
- Authors:
-
- ORNL
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1240540
- DOE Contract Number:
- AC05-00OR22725
- Resource Type:
- Conference
- Resource Relation:
- Conference: ACM Sigspatial - Seattle, California, United States of America - 11/3/2015 12:00:00 AM-11/6/2015 12:00:00 AM
- Country of Publication:
- United States
- Language:
- English
- Subject:
- PlanetSense; geo-spatial intelligence; big data architecture
Citation Formats
Thakur, Gautam Malviya, Bhaduri, Budhu, Piburn, Jesse, Sims, Kelly, Stewart, Robert, and Urban, Marie. PlanetSense: A Real-time Streaming and Spatio-temporal Analytics Platform for Gathering Geo-spatial Intelligence from Open Source Data. United States: N. p., 2015.
Web. doi:10.1145/2820783.2820882.
Thakur, Gautam Malviya, Bhaduri, Budhu, Piburn, Jesse, Sims, Kelly, Stewart, Robert, & Urban, Marie. PlanetSense: A Real-time Streaming and Spatio-temporal Analytics Platform for Gathering Geo-spatial Intelligence from Open Source Data. United States. https://doi.org/10.1145/2820783.2820882
Thakur, Gautam Malviya, Bhaduri, Budhu, Piburn, Jesse, Sims, Kelly, Stewart, Robert, and Urban, Marie. 2015.
"PlanetSense: A Real-time Streaming and Spatio-temporal Analytics Platform for Gathering Geo-spatial Intelligence from Open Source Data". United States. https://doi.org/10.1145/2820783.2820882. https://www.osti.gov/servlets/purl/1240540.
@article{osti_1240540,
title = {PlanetSense: A Real-time Streaming and Spatio-temporal Analytics Platform for Gathering Geo-spatial Intelligence from Open Source Data},
author = {Thakur, Gautam Malviya and Bhaduri, Budhu and Piburn, Jesse and Sims, Kelly and Stewart, Robert and Urban, Marie},
abstractNote = {Geospatial intelligence has traditionally relied on the use of archived and unvarying data for planning and exploration purposes. In consequence, the tools and methods that are architected to provide insight and generate projections only rely on such datasets. Albeit, if this approach has proven effective in several cases, such as land use identification and route mapping, it has severely restricted the ability of researchers to inculcate current information in their work. This approach is inadequate in scenarios requiring real-time information to act and to adjust in ever changing dynamic environments, such as evacuation and rescue missions. In this work, we propose PlanetSense, a platform for geospatial intelligence that is built to harness the existing power of archived data and add to that, the dynamics of real-time streams, seamlessly integrated with sophisticated data mining algorithms and analytics tools for generating operational intelligence on the fly. The platform has four main components – i) GeoData Cloud – a data architecture for storing and managing disparate datasets; ii) Mechanism to harvest real-time streaming data; iii) Data analytics framework; iv) Presentation and visualization through web interface and RESTful services. Using two case studies, we underpin the necessity of our platform in modeling ambient population and building occupancy at scale.},
doi = {10.1145/2820783.2820882},
url = {https://www.osti.gov/biblio/1240540},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Nov 01 00:00:00 EDT 2015},
month = {Sun Nov 01 00:00:00 EDT 2015}
}
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