Online Publication Date2018-10-25
Print Publication Date2018-04
Permanent link to this recordhttp://hdl.handle.net/10754/631621
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AbstractMassive 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.
CitationChen L, Shang S, Zhang Z, Cao X, Jensen CS, et al. (2018) Location-Aware Top-k Term Publish/Subscribe. 2018 IEEE 34th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2018.00073.
SponsorsThis work was supported by the grant of the Hong Kong Research Grants Council, Hong Kong SAR, China, No. 12258116 and the Nation Nature Science Foundation of China, China, No. 61602395.
Conference/Event name34th IEEE International Conference on Data Engineering, ICDE 2018