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 language | English |
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Title of host publication | Proceedings of the IEEE 31st International Conference on Data Engineering (ICDE) |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 255-266 |
Number of pages | 12 |
ISBN (Electronic) | 9781479979646 |
DOIs | |
Publication status | Published - 17 Apr 2015 |
Event | 2015 IEEE 31st International Conference on Data Engineering (ICDE) - Seoul, Korea, Republic of Duration: 13 Apr 2015 → 17 Apr 2015 |
Conference
Conference | 2015 IEEE 31st International Conference on Data Engineering (ICDE) |
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Country/Territory | Korea, Republic of |
City | Seoul |
Period | 13/04/2015 → 17/04/2015 |