SURGE: Continuous Detection of Bursty Regions Over a Stream of Spatial Objects

TL;DR

This paper introduces a novel method for continuously detecting bursty regions in streaming spatial data, addressing real-world problems like surge pricing and disease outbreaks with efficient exact and approximate solutions.

Contribution

It presents the first continuous bursty region detection framework with exact and approximate algorithms, including top-k detection, validated by extensive real-world experiments.

Findings

The proposed algorithms effectively detect bursty regions in real-time.

Approximate solutions achieve a ratio of (1-α)/4 in burst score.

Experiments demonstrate high efficiency and accuracy in real-world scenarios.

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 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 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 $\frac{1-\alpha}{4}$ in terms of the burst score, where $\alpha$ 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.