Subscription-based data aggregation techniques for top-k monitoring queries

Udomlamlert, Kamalas; Hara, Takahiro; Nishio, Shojiro

doi:10.1007/s11280-016-0385-1

Cited by 9 publications

(1 citation statement)

References 30 publications

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“…Udomlamlert et al [32] describe a Kmax method to balance re-evaluation cost and cache consumption. Instead of maintaining exactly the Top-k results, they maintain top-k' results.…”

Section: Dynamic Top-k Maintenancementioning

confidence: 99%

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

2018

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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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“…Udomlamlert et al [32] describe a Kmax method to balance re-evaluation cost and cache consumption. Instead of maintaining exactly the Top-k results, they maintain top-k' results.…”

Section: Dynamic Top-k Maintenancementioning

confidence: 99%