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Title: SURGE: continuous detection of bursty regions over a stream of spatial objects
Authors: Feng, Kaiyu
Guo, Tao
Cong, Gao
Bhowmick, Sourav S.
Ma, Shuai
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Feng, K., Guo, T., Cong, G., Bhowmick, S. S. & Ma, S. (2019). SURGE: continuous detection of bursty regions over a stream of spatial objects. IEEE Transactions On Knowledge and Data Engineering, 32(11), 2254-2268.
Project: MOE2016-T2-1-137
Journal: IEEE Transactions on Knowledge and Data Engineering
Abstract: With the proliferation of mobile devices and location-based services, continuous generation of massive volume of streaming spatial objects (i.e., geo-tagged data) opens up new opportunities to address real-world problems by analyzing them. In this paper, we present a novel continuous bursty region detection (surge) problem that aims to continuously detect a bursty region of a given size in a specified geographical area from a stream of spatial objects. Specifically, a bursty region shows maximum spike in the number of spatial objects in a given time window. The surge problem is useful in addressing several real-world challenges such as surge pricing problem in online transportation and disease outbreak detection. To solve the problem, we propose an exact solution and two approximate solutions, and the approximation ratio is 1-α4/4 in terms of the burst score, where α is a parameter to control the burst score. We further extend these solutions to support detection of top-k bursty regions. Extensive experiments with real-world data are conducted to demonstrate the efficiency and effectiveness of our solutions.
ISSN: 1041-4347
DOI: 10.1109/TKDE.2019.2915654
Schools: School of Computer Science and Engineering 
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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