Parallelizing top-k frequent spatiotemporal terms computation on key-value stores

Le, Hong Van; Takasu, Atsuhiro

doi:10.1145/3274895.3274956

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2018

DOI: 10.1145/3274895.3274956

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Parallelizing top-k frequent spatiotemporal terms computation on key-value stores

Hong Van Le

Atsuhiro Takasu

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2020

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Top-k term publish/subscribe for geo-textual data streams

et al. 2020

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Massive amounts of data that contain spatial, textual, and temporal information are being generated at a rapid pace. With streams of such data, which includes checkins and geo-tagged tweets, available, users may be interested in being kept up-to-date on which terms are popular in the streams in a particular region of space. To enable this functionality, we aim at efficiently processing two types of general top-k term subscriptions over streams of spatio-temporal documents: Region-based Top-k Spatial-Temporal Term (RST) subscriptions and Similarity-based Top-k Spatial-Temporal Term (SST) subscriptions. RST subscriptions continuously maintain the top-k most popular trending terms within a user-defined region. SST subscriptions free users from defining a region and maintain top-k locally popular terms based on a ranking function that com-

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Top-k term publish/subscribe for geo-textual data streams

et al. 2020

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Parallelizing top-k frequent spatiotemporal terms computation on key-value stores

Cited by 1 publication

References 10 publications

Top-k term publish/subscribe for geo-textual data streams

Top-k term publish/subscribe for geo-textual data streams

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