Temporal Spatial-Keyword Top-k publish/subscribe

Lisi Chen, Gao Cong, Xin Cao, Kian-Lee Tan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

127 Citations (Scopus)


Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale. These geo-textual data cover a wide range of topics. Users are interested in receiving up-to-date tweets such that their locations are close to a user specified location and their texts are interesting to users. For example, a user may want to be updated with tweets near her home on the topic “food poisoning vomiting.” We consider the Temporal Spatial-Keyword Top-k Subscription (TaSK) query. Given a TaSK query, we continuously maintain up-to-date top-k most relevant results over a stream of geo-textual objects (e.g., geo-tagged Tweets) for the query. The TaSK query takes into account text relevance, spatial proximity, and recency of geo-textual objects in evaluating its relevance with a geo-textual object. We propose a novel solution to efficiently process a large number of TaSK queries over a stream of geotextual objects. We evaluate the efficiency of our approach on two real-world datasets and the experimental results show that our solution is able to achieve a reduction of the processing time by 70-80% compared with two baselines.
Original languageEnglish
Title of host publicationProceedings of the IEEE 31st International Conference on Data Engineering (ICDE)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781479979646
Publication statusPublished - 17 Apr 2015
Event2015 IEEE 31st International Conference on Data Engineering (ICDE) - Seoul, Korea, Republic of
Duration: 13 Apr 201517 Apr 2015


Conference2015 IEEE 31st International Conference on Data Engineering (ICDE)
Country/TerritoryKorea, Republic of


Dive into the research topics of 'Temporal Spatial-Keyword Top-k publish/subscribe'. Together they form a unique fingerprint.

Cite this