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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 $\frac{1-\alpha }{4}$ 1 - α 4 in terms of the burst score, where $\alpha$ α is a parameter to control the burst score. We further extend these solutions to support detection of top- $k$ k bursty regions. Extensive experiments with real-world data are conducted to demonstrate the efficiency and effectiveness of our solutions. [ABSTRACT FROM AUTHOR]

Computing shortest paths and distances is one of the fundamental problems on graphs, and it remains a challenging task today. This article investigates a light-weight data reduction technique for speeding-up shortest path and distance queries on large graphs. To do this, we propose a notion of routing proxies (or simply proxies), each of which represents a small subgraph, referred to as deterministic routing areas (dras). We first show that routing proxies hold good properties for speeding-up shortest path and distance queries. Then, we design a linear-time algorithm to compute routing proxies and their corresponding dras. Finally, we experimentally verify that our solution is a general technique for reducing graph sizes and speeding-up shortest path and distance queries, using real-life large graphs. [ABSTRACT FROM PUBLISHER]