An Efficient Location-Aware Top-k Subscription Matching for Publish/Subscribe with Boolean Expressions

Jiang, Hanhan; Sheng, Victor S.; Xu, Jiajie; Liu, An; Wu, Jian; Cui, Zhiming

doi:10.1007/978-3-319-32049-6_21

Cited by 5 publications

(1 citation statement)

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“…Unlike traditional publish/subscribe systems, geo-textual data includes a geographic description and a textual description that are continuously generated and propagated in the form of a dynamic data stream [11]. Geo-textual data has strong position sensitivity (for example, when a subscriber leaves a geographic area, the geo-related publication data is no longer sent to the subscriber) [12]. Therefore, higher efficiency and flexibility requirements have been proposed for more and more application scenarios [13].…”

Section: Introductionmentioning

confidence: 99%

EFTG: Efficient and Flexible Top-K Geo-textual Publish/Subscribe

2018

KSII TIIS

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With the popularity of mobile networks and smartphones, geo-textual publish/subscribe messaging has attracted wide attention. Different from the traditional publish/subscribe format, geo-textual data is published and subscribed in the form of dynamic data flow in the mobile network. The difference creates more requirements for efficiency and flexibility. However, most of the existing Top-k geo-textual publish/subscribe schemes have the following deficiencies: (1) All publications have to be scored for each subscription, which is not efficient enough. (2) A user should take time to set a threshold for each subscription, which is not flexible enough. Therefore, we propose an efficient and flexible Top-k geo-textual publish/subscribe scheme. First, our scheme groups publish and subscribe based on text classification. Thus, only a few parts of related publications should be scored for each subscription, which significantly enhances efficiency. Second, our scheme proposes an adaptive publish/subscribe matching algorithm. The algorithm does not require the user to set a threshold. It can adaptively return Top-k results to the user for each subscription, which significantly enhances flexibility. Finally, theoretical analysis and experimental evaluation verify the efficiency and effectiveness of our scheme.

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Section: Introductionmentioning

confidence: 99%