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GeoTrie: A Scalable Architecture for Location-Temporal Range Queries over Massive GeoTagged Data Sets

Abstract : The proliferation of GPS-enabled devices leads to the massive generation of geotagged data sets recently known as Big Location Data. It allows users to explore and analyse data in space and time, and requires an architecture that scales with the insertions and location-temporal queries workload from thousands to millions of users. Most large scale key-value data storage solutions only provide a single one-dimensional index which does not natively support efficient multidimensional queries. In this paper, we propose GeoTrie, a scalable architecture built by coalescing any number of machines organized on top of a Distributed Hash Table. The key idea of our approach is to provide a distributed global index which scales with the number of nodes and provides natural load balancing for insertions and location-temporal range queries. We assess our solution using the largest public multimedia data set released by Yahoo! which includes millions of geotagged multimedia files.
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https://hal.inria.fr/hal-01388949
Contributor : Pierre Sens <>
Submitted on : Thursday, October 27, 2016 - 5:43:47 PM
Last modification on : Wednesday, September 30, 2020 - 4:50:03 PM

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  • HAL Id : hal-01388949, version 1

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Rudyar Cortés, Xavier Bonnaire, Olivier Marin, Luciana Arantes, Pierre Sens. GeoTrie: A Scalable Architecture for Location-Temporal Range Queries over Massive GeoTagged Data Sets. The 15th IEEE International Symposium on Network Computing and Applications (NCA 2016), 2016, Cambridge, MA, United States. ⟨hal-01388949⟩

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