Affiliation:
1. University of New South Wales, Australia
2. University of Technology Sydney, Australia
3. East China Normal University, China and University of New South Wales, Australia
Abstract
As the prevalence of social media and GPS-enabled devices, a massive amount of
geo-textual
data has been generated in a stream fashion, leading to a variety of applications such as location-based recommendation and information dissemination. In this paper, we investigate a novel real-time top-
k
monitoring problem over sliding window of streaming data; that is, we continuously maintain the
top-k
most relevant
geo-textual messages
(e.g., geo-tagged tweets) for a large number of
spatial-keyword subscriptions
(e.g., registered users interested in
local events
) simultaneously. To provide the most recent information under controllable memory cost, sliding window model is employed on the streaming geo-textual data. To the best of our knowledge, this is the first work to study top-
k
spatial-keyword publish/subscribe over sliding window. A novel system, called Skype (Top-k Spatial-keyword Publish/Subscribe), is proposed in this paper. In Skype, to continuously maintain top-
k
results for massive subscriptions, we devise a novel indexing structure upon subscriptions such that each incoming message can be immediately delivered on its arrival. Moreover, to reduce the expensive top-
k
re-evaluation cost triggered by message expiration, we develop a novel
cost-based k-skyband
technique to reduce the number of re-evaluations in a cost-effective way. Extensive experiments verify the great efficiency and effectiveness of our proposed techniques.
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Cited by
63 articles.
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