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2018 IEEE 34th International Conference on Data Engineering (ICDE) (2018)
Paris, France
Apr 16, 2018 to Apr 19, 2018
ISSN: 2375-026X
ISBN: 978-1-5386-5520-7
pp: 749-760
ABSTRACT
Massive amount of data that contain spatial, textual, and temporal information are being generated at a high scale. These spatio-temporal documents cover a wide range of topics in local area. Users are interested in receiving local popular terms from spatio-temporal documents published with a specified region. We consider the Top-k Spatial-Temporal Term (ST2) Subscription. Given an ST2 subscription, we continuously maintain up-to-date top-k most popular terms over a stream of spatio-temporal documents. The ST2 subscription takes into account both frequency and recency of a term generated from spatio-temporal document streams in evaluating its popularity. We propose an efficient solution to process a large number of ST2 subscriptions over a stream of spatio-temporal documents. The performance of processing ST2 subscriptions is studied in extensive experiments based on two real spatio-temporal datasets.
INDEX TERMS
document handling, location based services, message passing, middleware
CITATION

L. Chen, S. Shang, Z. Zhang, X. Cao, C. S. Jensen and P. Kalnis, "Location-Aware Top-k Term Publish/Subscribe," 2018 IEEE 34th International Conference on Data Engineering (ICDE), Paris, France, 2018, pp. 749-760.
doi:10.1109/ICDE.2018.00073
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