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 (ST~2) Subscription. Given an ST~2 subscription, we continuously maintain up-to-date top-k most popular terms over a stream of spatio-temporal documents. The ST~2 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 ST~2 subscriptions over a stream of spatio-temporal documents. The performance of processing ST~2 subscriptions is studied in extensive experiments based on two real spatio-temporal datasets.
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