Project:A
Real-Time Data System for Hybrid and Continuous Query Processing across
Diverse Data Modalities
Selected
publications:
- NEXT: A New
Secondary Index Framework for LSM-based Data Storage. In SIGMOD
2025
Project:
Data System for Point Cloud Data Analytics
Selected
publications:
- MAST: Towards
Efficient Analytical Query Processing on Point Cloud Data. In SIGMOD
2025
Project:
Querying spatio-textual
(geo-textual) data (Spatial
keyword queries or Geographical Information Retrieval)
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Figure
1: Research overview
on geo-spatial+X data
Survey:
Location-
and
Keyword-based querying of geo-textual data (VLDB
Journal, 2021).
Selected
publications:
- Spatail
+ Vector
Search, SIGMOD'23, SIGMOD'25, PVLDB'25, VLDBJ'25
- 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(unpublished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.
Project: Region
search,
exploration, representation, and recommendation
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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
Project:
Trajectory data management and mining
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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
-
BT-Tree: A
Reinforcement Learning Based Index for Big Trajectory Data. In SIGMOD
2025
- 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
Acknowledgement:
Some of our projects are supported by grants awarded by Ministry of
Education,
NRF, IAF, Singtel/NCS, Roll-Royce, Alibaba, and Microsoft.