Dingxiong Deng

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Computing the shortest path between two given locations in a road network is an important problem that finds applications in various map services and commercial navigation products. The stateof-the-art solutions for the problem can be divided into two categories: spatial-coherence-based methods and vertex-importancebased approaches. The two categories of(More)
With the progress of mobile devices and wireless broadband, a new <i>eMarket</i> platform, termed <i>spatial crowdsourcing</i> is emerging, which enables workers (aka crowd) to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) posted by a <i>requester</i>. In this paper, we study a version of the spatial crowd-sourcing(More)
A new platform, termed spatial crowdsourcing, is emerging which enables a requester to commission workers to physically travel to some specified locations to perform a set of spatial tasks (i.e., tasks related to a geographical location and time). The current approach is to formulate spatial crowdsourcing as a matching problem between tasks and workers;(More)
In this paper, we propose a new framework for opinion summarization based on sentence selection. Our goal is to assist users to get helpful opinion suggestions from reviews by only reading a short summary with few informative sentences, where the quality of summary is evaluated in terms of both aspect coverage and viewpoints preservation. More specifically,(More)
With the vast availability of traffic sensors from which traffic information can be derived, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning, etc. One key challenge in traffic prediction is how much to rely on prediction models that are(More)
Real-time ride-sharing, which enables on-the-fly matching between riders and drivers (even en-route), is an important problem due to its environmental and societal benefits. With the emergence of many ride-sharing platforms (e.g., Uber and Lyft), the design of a scalable framework to match riders and drivers based on their various constraints while(More)
With the progress of mobile devices and wireless broadband, a new eMarket platform, termed spatial crowdsourcing is emerging, which enables workers (aka crowd) to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) posted by a requester. In this paper, we study a version of the spatial crowdsourcing problem in which the(More)
With the popularity of mobile devices, Spatial Crowdsourcing (SC) is emerging as a new framework that enables human workers to perform tasks in the physical world. With spatial crowdsourcing, the goal is to outsource a set of spatiotemporal tasks (i.e., tasks with time and location) to a set of workers, which requires the workers to be physically present at(More)
Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamism associated with changing road conditions. In this paper, we propose a Latent Space Model for Road(More)
The delivery and courier services are entering a period of rapid change due to the recent technological advancements, E-commerce competition and crowdsourcing business models. These revolutions impose new challenges to the well studied vehicle routing problem by demanding a) more ad-hoc and near real time computation as opposed to nightly batch jobs of(More)