Our Research Topics

1. Geospatial+X data management 
2. Spatial data mining and its applications in smart city 

1. Geospatial+X data management 

                         Figure 1: Research overview on geo-spatial+X data

1.1 Querying spatio-textual (geo-textual) data (Spatial keyword queries)

Survey:

Location- and Keyword-based querying of geo-textual data  (VLDB Journal, 2021). We would like to update the survey regularly, and if we miss your work on the topic, please drop us an email.

Selected publications:

• SSTD: A Distributed System on Streaming Spatio-Textual Data, PVLDB 2020
• STAR: A Distributed Stream Warehouse System for Spatial Data. SIGMOD Conference 2020: 2761-2764 (Demo)
• Distributed Publish/Subscribe Query Processing on the Spatio-Textual Data Stream (ICDE 17)
• Diversity-aware top-k publish/subscribe on text stream (SIGMOD 15)
• Temporal spatial-keyword top-k publish/subscribe on geo-textual data stream (ICDE15 and demo in VLDB14)
• Boolean spatial-keyword publish/subscribe on geo-textual data stream (SIGMOD13  and demo in VLDB14)

• On Spatial Pattern Matching (ICDE’17, VLDBJ’19)
• Answering the m-closest keywords query (SIGMOD 15)
• Search regions of interest for user exploration  (VLDB14)
• Distributed spatial keyword querying on road networks (EDBT14)
• An evaluation of 12 geo-spatial indexes (VLDB13). Code available here. 
• An overview paper on spatial-keyword querying (invited paper in ER)
• Route planning: answering queries like “a most popular route such that it passes by shopping malls, restaurant, and pub, and the travel time is within 4 hours.” (PVLDB12)
• Efficient processing of several types of spatial keyword queries (VLDB09, PVLDB10, SIGMOD11a).  Code for our SIGMOD11 paper is available here.  An extension of our SIGMOD 11 paper is published in TODS
• Efficient algorithms and cost models for reverse spatial-keyword k-nearest neighbor search (SIGMOD11b, TODS14)
• Efficient spatial keyword search in trajectory databases (unpublished paper)

Main contributors: Xin Cao, Lisi Chen, Zhida Chen, Yue Chen, Shang Liu

Collaborators: Christian S. Jensen, Walid Aref.

1.2 Region search, exploration, representation, and recommendation

                Figure 2: Overview of our research on region

Selected publications:

• SURGE: Continuous Detection of Bursty Regions Over a Stream of Spatial Objects (TKDE19, ICDE18)
• Finding attribute-aware similar regions for data analysis (PVLDB 19)
• Efficient Similar Region Search with Deep Metric Learning (KDD 18)
• Efficient Selection of Geospatial Data on maps for Interactive Visualized Exploration (SIGMOD 18)
• Towards Best Region Search for Data Exploration (SIGMOD 2016)
• Topic Exploration in Spatio-Temporal Document Collections(SIGMOD 2016, VLDBJ19)
• Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns (IJCAI, 2018)
• Efficient Similar Region Search with Deep Metric Learning (KDD 2018)

Main contributors: Xin Cao, Kaiyu Feng, Kaiqi Zhao, Yiding Liu, Yi Li

Collaborators: Cheng Long

1.3 Trajectory data management and mining

                Figure 3: Resarch overview on trajectory data

Survey:

A Survey on Trajectory Data Management, Analytics, and Learning (ACM Comput. Surv., 2021)

Selected Publications:

similarity and search

• Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning, PVLDB 2020 
• Deep Learning for Trajectory Similarity Search (ICDE18, ICDE19)
• Effective and Efficient Sports Play Retrieval with Deep Representation Learning (KDD19)

Main contributors: Kaiyu Feng, Kaiqi Zhao, Di Yao, Yiding Liu, Xiucheng Li, Zheng Wang

Collaborators: Zhifeng Bao, Cheng  Long

2. Spatial-temporal data mining and its applications in smart city

2.1 Road network representation, trajectory data mining, time series data mining

Selected Publications:

• Robust Road Network Representation Learning: When Traffic Patterns Meet Traveling Semantics, CIKM2021 
• Trajectory simplification: ICDE 2021, KDD 2021
• Online Anomalous Trajectory Detection with Deep Generative Sequence Modeling, ICDE2020
• Spatial Transition Learning on Road Networks with Deep Probabilistic Models, ICDE 2020  
• Learning Travel Time Distributions with Deep Generative Model. (WWW 2019)

Main contributors: Kaiqi Zhao, Di Yao, Xiucheng Li,  Zheng Wang, Yile Chen, Yue Jiang, Shuai Liu, Hettige Kethmi Hirushini 

Collaborators: Zhifeng Bao, Cheng Long

2.2 POI data mining and region data mining

• See other projects

Acknowledgement: Some of our projects are supported by grants awarded by Ministry of Education, NRF, IAF, Singtel/NCS, Roll-Royce, Alibaba, and Microsoft.