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

96 Citations (Scopus)

Abstract

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 (IEEE)
Pages255-266
Number of pages12
ISBN (Electronic)9781479979646
DOIs
Publication statusPublished - 17 Apr 2015
Event2015 IEEE 31st International Conference on Data Engineering (ICDE) - Seoul, Korea, Republic of
Duration: 13 Apr 201517 Apr 2015

Conference

Conference2015 IEEE 31st International Conference on Data Engineering (ICDE)
CountryKorea, Republic of
CitySeoul
Period13/04/201517/04/2015

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