K
Kai Zheng
Researcher at University of Electronic Science and Technology of China
Publications - 153
Citations - 5328
Kai Zheng is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Query optimization & Trajectory. The author has an hindex of 35, co-authored 142 publications receiving 4295 citations. Previous affiliations of Kai Zheng include University of Queensland & Soochow University (Suzhou).
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Journal ArticleDOI
Discovering Urban Functional ZonesUsing Latent Activity Trajectories
TL;DR: This paper introduces the concept of latent activity trajectory (LAT), which captures socioeconomic activities conducted by citizens at different locations in a chronological order, and develops a topic-modeling-based approach to cluster the segmented regions into functional zones leveraging mobility and location semantics mined from LAT.
Proceedings ArticleDOI
Reducing Uncertainty of Low-Sampling-Rate Trajectories
TL;DR: A systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process and demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.
Proceedings ArticleDOI
On discovery of gathering patterns from trajectories
TL;DR: This work proposes a novel concept, called gathering, which is a trajectory pattern modelling various group incidents such as celebrations, parades, protests, traffic jams and so on, and develops a set of well thought out techniques to improve the performance.
Journal ArticleDOI
Adapting to User Interest Drift for POI Recommendation
TL;DR: This paper proposes a latent class probabilistic generative model Spatial-Temporal LDA (ST-LDA) to learn region-dependent personal interests according to the contents of their checked-in POIs at each region, and designs an effective attribute pruning algorithm to overcome the curse of dimensionality and support fast online recommendation for large-scale POI data.
Journal ArticleDOI
Online Discovery of Gathering Patterns over Trajectories
TL;DR: This work proposes a novel concept, called gathering, which is a trajectory pattern modeling various group incidents such as celebrations, parades, protests, traffic jams and so on, and proposes an online discovery solution by applying a series of optimization schemes, which can keep track of gathering patterns while new trajectory data arrive.