SIGMOD 2026 | 时空数据(Spatial-Temporal)论文总结【轨迹压缩，轨迹相似度计算，地理空间数据库，多模态大模型，数据清洗等】

SIGMOD 2026在2026年5月31日至6月5日在印度班加罗尔（Bengaluru, India）正在举行。

本文总结了SIGMOD 2026有关时空数据（Spatial Temporal）的相关文章，共计5篇，如有疏漏，欢迎补充。

时空数据Topic：轨迹压缩，轨迹相似度计算，地理空间数据库，多模态大模型，数据清洗等

1 DimWeaver: Dimensional Weaved Trajectory Compression

链接：https://dl.acm.org/doi/10.1145/3802040

代码：https://github.com/xkitsios/DimWeaver

作者：Xenophon Kitsios (Athens University of Economics and Business)*; Panagiotis Liakos (Athens University of Economics and Business); Katia Papakonstantinopoulou (Athens University of Economics and Business); Nikos Giatrakos (Technical University of Crete); Yannis Kotidis (Athens University of Economics and Business)

关键词：轨迹压缩

2 GoodTP: An Effective Data Selection Framework for Enhancing Trajectory Similarity Learning via Monte Carlo Tree Search

链接：https://dl.acm.org/doi/10.1145/3802067

代码：https://github.com/yuanhaitao/GoodTP/t

作者：Haitao Yuan (Nanyang Technological University)*; Gao Cong (Nanyang Technological University)

关键词：数据选择，轨迹相似度计算

3 GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases

链接：https://dl.acm.org/doi/abs/10.1145/3769796

作者：Zhihan Zheng (Beijing University of Posts and Telecommunications)*; Haitao Yuan (Nanyang Technological University); Minxiao Chen (Beijing University of Posts and Telecommunications); Nan Jiang (Beijing University of Posts and Telecommunications); Haoning Wang (National University of Singapore); Shangguang Wang (Beijing University of Posts and Telecommunications)

关键词：多模态大模型，零样本知识图谱补全，地理空间数据库

4 3dSAGER: Geospatial Entity Resolution over 3D Objects

链接：https://dl.acm.org/doi/10.1145/3769751

代码：https://github.com/BarGenossar/3dSAGER

作者：Bar Genossar (Technion -- Israel Institute of Technology)*; Sagi Dalyot (Technion -- Israel Institute of Technology); Roee Shraga (Worcester Polytechnic Institute); Avigdor Gal (Technion -- Israel Institute of Technology)

关键词：地理空间实体解析，实体匹配，块划分

5 From Suspicious Errors to Valid Data: On Repairing Spatio-Temporal Data via Spatial and Temporal Dependencies

链接：https://dl.acm.org/doi/abs/10.1145/3769794

代码：https://github.com/cookiesvivi/STRhub

作者：Weiwei Deng (Nankai University); Yu Sun (Nankai University)*; Shaoxu Song (Tsinghua University); Xiaojie Yuan (Nankai University)

关键词：时空数据，数据修复，数据清洗