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  1. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial '12)
  2. Towards scalable ad-hoc climate anomalies search
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Spatiotemporal data mining in the era of big spatial data: algorithms and applications
EarthDB: scalable analysis of MODIS data using SciDB
Big 3D spatial data processing using cloud computing environment
Speeding up large-scale point-in-polygon test based spatial join on GPUs
Elastic and effective spatio-temporal query processing scheme on Hadoop
Accelerating satellite image based large-scale settlement detection with GPU
Computing the drainage network on huge grid terrains
Sort-based parallel loading of R-trees
TMC-pattern: holistic trajectory extraction, modeling and mining
Predictive analytics with surveillance big data
Extracting storm-centric characteristics from raw rainfall data for storm analysis and mining
Towards scalable ad-hoc climate anomalies search

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Towards scalable ad-hoc climate anomalies search

Content Provider ACM Digital Library
Author Baumann, Peter Misev, Dimitar
Abstract Meteorological data contribute significantly to "Big Data"; however, not only is their volume ranging into Petabyte sizes for single objects a challenge, but also the number of dimensions -- such general 4-D spatio-temporal data cannot be handled through traditional GIS methods and tools. Actually, climate data tend to transcend these dimensions and add an extra time dimension for the simulation run time, ending up with 5-D data cubes. Traditional databases, known for their flexibility and scalability, have proven inadequate due to their lack of support for multi-dimensional rasters. Consequently, file-based implementations are being used for serving such data to the community, rather than databases. This is recently overcome by Array Databases which provide storage and query support for this information category of multi-dimensional rasters, thereby unleashing the scalability and flexibility advantages for climate data management. In this contribution, we present a case study where non-trivial analytics functionality on n-D climate data cubes has been established. Storage optimization techniques novel to standard databases allow to tune the system for interactive response in many cases. We briefly introduce the rasdaman database system used, present the database schema and practically important queries use case, and report preliminary performance observations. To the best of our knowledge, this is the first non-academic, real-life deployment of an array database for up to 5-D data sets.
Starting Page 101
Ending Page 110
Page Count 10
File Format PDF
ISBN 9781450316927
DOI 10.1145/2447481.2447493
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-11-06
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Rasdaman Array databases Climate data Weather data
Content Type Text
Resource Type Article
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