Abstract
The amount of spatio-temporal data generated in numerous scientific and industrial settings have exploded in recent years. Without a distributed platform, supporting efficient analytics operations over such voluminous datasets become prohibitively expensive. As a result there has been an increasing interest in using map-reduce to parallelize the processing of large-scale spatio-temporal data. While Hadoop, which has become the de-facto implementation of map-reduce, has shown to be effective in handling large volumes of unstructured data, several key issues needs to be addressed to exploit its power for processing spatio-temporal data.
In this tutorial, we explore design techniques for spatio-temporal analytics and data management on Hadoop, based on recent work in this area. We outline strategies for devising map-reduce algorithms for performing fundamental spatial analytics involving computational geometry operations as well as two-way and multi-way spatial join operations. We discuss storage optimization techniques such as chunking and colocation to enable efficient organization of multi-dimensional data on HDFS along with indexing techniques for fast spatial data access.
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Gupta, H., Lakshminarasimhan, S. (2014). Processing Spatio-temporal Data On Map-Reduce. In: Srinivasa, S., Mehta, S. (eds) Big Data Analytics. BDA 2014. Lecture Notes in Computer Science, vol 8883. Springer, Cham. https://doi.org/10.1007/978-3-319-13820-6_5
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DOI: https://doi.org/10.1007/978-3-319-13820-6_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13819-0
Online ISBN: 978-3-319-13820-6
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