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


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 check-ins 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 spatio-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 combines term frequency, term recency, and term proximity. To solve the problem, we propose solutions that are capable of supporting real-life location-based publish/subscribe applications that process large numbers of SST and RST subscriptions over a realistic stream of spatio-temporal documents. The performance of our proposed solutions is studied in extensive experiments using two spatio-temporal datasets.

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  1. We say a subscription matches a term if the term is a top-k result of the subscription.

  2. The complexity is O(1) when the SF score is computed based on Euclidean distance or network distance with pre-computation of pair distances.

  3. We do not index \(d_4\) because it does not contain \(w_3\).

  4. Parameter M is set to 16–128 in experiments (cf. Sect. 5).


  6. The performance discrepancy between baseline and TS is negligible when k is small. Thus, we only report the result of baseline when varying k.



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This work was supported by the National Natural Science Foundation of China (61932004, 61922054, 61872235, 61729202, 61832017, U1636210), the National Key Research and Development Program of China (2018YFC1504504, 2016YFB0700502), and Hong Kong RGC Grant 12201018.

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Chen, L., Shang, S., Jensen, C.S. et al. Top-k term publish/subscribe for geo-textual data streams. The VLDB Journal 29, 1101–1128 (2020).

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  • Publish
  • Subscribe
  • Spatio-temporal
  • Keyword
  • Stream