Research
Topics
I am interested in Spatial Databases, with a particular focus on Spatial Crowdsourcing, Ridesharing, Spatial Advertising. Recently, I also work on Graph Databases, Blockchain and Knowledge Graph.
Code
I try to publish code promptly. You are welcome to contribute to the projects and cooperate with me and my team.
- Latex Template for CS papers: a simple latex template of cs papers for junior PG students. Many other useful materials are also put in the repository.
- gMission: a general spatial crowdsourcing platform.
- Spatial Crowdsourcing Dataset Generator: a toolbox to generate the synthetic datasets for spatial crowdsourcing applications.
- Benchmark for Task Assignment in Spatial Crowdsourcing: a benchmark to test the representitive algorithms for task assignment in spatial crowdsourcing. Details in our VLDB 2018 experiment analyses paper: An Experimental Evaluation of Task Assignment in Spatial Crowdsourcing.
- Queueing-Theoretic Framework for online Car-hailing: a framework to maximize the served requests of online car-hailing platforms using a queueing theoretic framework with DNN spatiotemporal prediction models. Details in our ICDE 2019 short paper: A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic Car-Hailing. An updated and enhanced framework for our VLDB 2021 full paper can be found here!
- Demand-Aware Route Planning for Shared Mobility Services: a framework to maximize the overall uitility of online ridesharing platforms using a demand-aware insertion route planning basic operator. Details in our VLDB 2020 paper: Demand-Aware Route Planning for Shared Mobility Services.
- GridTuner: a out-of-box tool to tune the size of grids for spatiotemporal prediction models to maximize the utility of the applications. Details in our ICDE 2022 paper: GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models.
- Efficient K-clique Listing: source code of our ICDE 2022 paper: Efficient k-clique Listing with Set Intersection Speedup.
- Efficient Non-Learning Similar Substrajectory Search: source code of our VLDB 2023 paper: Efficient Non-Learning Similar Subtrajectory Search, which proposes a novel algorithm to search for the exact most-similar subtrajectory from a set of data trajectory for the given query trajectory under most widely used distance functions.
See the licensing terms within each project’s codebase for the requisite legal details.