Zobrazeno 1 - 10
of 721
pro vyhledávání: '"Jensen, Christian S."'
Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being a pair of a high-dimensional vector and a numeric value, an RFANN query with a vector and
Externí odkaz:
http://arxiv.org/abs/2409.02571
Autor:
Lin, Yan, Zhou, Zeyu, Liu, Yicheng, Lv, Haochen, Wen, Haomin, Li, Tianyi, Li, Yushuai, Jensen, Christian S., Guo, Shengnan, Lin, Youfang, Wan, Huaiyu
Spatio-temporal (ST) trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent anal
Externí odkaz:
http://arxiv.org/abs/2407.12550
The availability of massive vehicle trajectory data enables the modeling of road-network constrained movement as travel-cost distributions rather than just single-valued costs, thereby capturing the inherent uncertainty of movement and enabling impro
Externí odkaz:
http://arxiv.org/abs/2407.06881
Autor:
Zhang, Qianru, Wang, Haixin, Long, Cheng, Su, Liangcai, He, Xingwei, Chang, Jianlong, Wu, Tailin, Yin, Hongzhi, Yiu, Siu-Ming, Tian, Qi, Jensen, Christian S.
This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. With the advancements in RNNs, CNNs, and other non-generative techniques
Externí odkaz:
http://arxiv.org/abs/2405.09592
Autor:
Miao, Hao, Zhao, Yan, Guo, Chenjuan, Yang, Bin, Zheng, Kai, Huang, Feiteng, Xie, Jiandong, Jensen, Christian S.
The widespread deployment of wireless and mobile devices results in a proliferation of spatio-temporal data that is used in applications, e.g., traffic prediction, human mobility mining, and air quality prediction, where spatio-temporal prediction is
Externí odkaz:
http://arxiv.org/abs/2404.14999
We are witnessing an increasing availability of streaming data that may contain valuable information on the underlying processes. It is thus attractive to be able to deploy machine learning models on edge devices near sensors such that decisions can
Externí odkaz:
http://arxiv.org/abs/2404.13990
Autor:
Qiu, Xiangfei, Hu, Jilin, Zhou, Lekui, Wu, Xingjian, Du, Junyang, Zhang, Buang, Guo, Chenjuan, Zhou, Aoying, Jensen, Christian S., Sheng, Zhenli, Yang, Bin
Time series are generated in diverse domains such as economic, traffic, health, and energy, where forecasting of future values has numerous important applications. Not surprisingly, many forecasting methods are being proposed. To ensure progress, it
Externí odkaz:
http://arxiv.org/abs/2403.20150
Publikováno v:
ICDE 2024
Graph augmentation with contrastive learning has gained significant attention in the field of recommendation systems due to its ability to learn expressive user representations, even when labeled data is limited. However, directly applying existing G
Externí odkaz:
http://arxiv.org/abs/2403.16656
Cyber-physical system sensors emit multivariate time series (MTS) that monitor physical system processes. Such time series generally capture unknown numbers of states, each with a different duration, that correspond to specific conditions, e.g., "wal
Externí odkaz:
http://arxiv.org/abs/2402.14041
Autor:
Lin, Yan, Hu, Jilin, Guo, Shengnan, Yang, Bin, Jensen, Christian S., Lin, Youfang, Wan, Huaiyu
Vehicle movement is frequently captured in the form of trajectories, i.e., sequences of timestamped locations. Numerous methods exist that target different tasks involving trajectories such as travel-time estimation, trajectory recovery, and trajecto
Externí odkaz:
http://arxiv.org/abs/2402.07232