Temporal Spatial-Keyword Top-k publish/subscribe
Data(s) |
17/04/2015
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Institute of Electrical and Electronics Engineers (IEEE) |
Direitos |
info:eu-repo/semantics/restrictedAccess |
Fonte |
Chen , L , Cong , G , Cao , X & Tan , K-L 2015 , Temporal Spatial-Keyword Top-k publish/subscribe . in Proceedings of the IEEE 31st International Conference on Data Engineering (ICDE) . Institute of Electrical and Electronics Engineers (IEEE) , pp. 255-266 , 2015 IEEE 31st International Conference on Data Engineering (ICDE) , Seoul , Korea, Republic of , 13-17 April . DOI: 10.1109/ICDE.2015.7113289 |
Tipo |
contributionToPeriodical |