2018 IEEE 34th International Conference on Data Engineering (ICDE) (2018)
Apr 16, 2018 to Apr 19, 2018
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.
document handling, location based services, message passing, middleware
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.