Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Kunqing Xie"'
Publikováno v:
Data Science Journal, Vol 6 (2007)
Grid computing for resources sharing and distributed computing has been researched widely in the past. As for distributed spatial datasets, the current centralized administrative scheme may become the system performance bottleneck. This paper present
Externí odkaz:
https://doaj.org/article/41b9f24cefe342a8a3b0d5b151dad7ee
Autor:
Cuo Cai, Kunqing Xie
Publikováno v:
Data Science Journal, Vol 6 (2007)
Currently there are many methods of collecting geoscience data, such as station observations, satellite images, sensor networks, etc. All of these data sources from different regions and time intervals are combined in geoscience research activities t
Externí odkaz:
https://doaj.org/article/8c27ea4a3135433f8983c9e296f3068b
Publikováno v:
Data Science Journal, Vol 6 (2007)
Because of the development of modern-day satellites and other data acquisition systems, global climate research often involves overwhelming volume and complexity of high dimensional datasets. As a data preprocessing and analysis method, the clusterin
Externí odkaz:
https://doaj.org/article/54e37d24f3ed4104a2fbbc308ec31ead
Publikováno v:
Data Science Journal, Vol 6 (2007)
In this paper, we apply data mining technologies to a 100-year global land precipitation dataset and a 100-year Sea Surface Temperature (SST) dataset. Some interesting teleconnections are discovered, including well-known patterns and unknown patterns
Externí odkaz:
https://doaj.org/article/05bd806181274a978ff76b709f8fe33a
Publikováno v:
Data Science Journal, Vol 6 (2007)
Teleconnection is a linkage between two climate events that occur in widely separated regions of the globe on a monthly or longer timescale. In the past, statistical methods have been used to discover teleconnections. However, because of the overwhel
Externí odkaz:
https://doaj.org/article/bf6d2b63b1c341039948fe89bd726f19
Publikováno v:
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining.
Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks. However, the noisy information behind the real-world networks and the overfitting problem both negatively impa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88296821a8e2c118a03ba52fcc17e7e5
https://doi.org/10.1145/3488560.3498474
https://doi.org/10.1145/3488560.3498474
Publikováno v:
IJCAI
With the rapid growth of traffic sensors deployed, a massive amount of traffic flow data are collected, revealing the long-term evolution of traffic flows and the gradual expansion of traffic networks. How to accurately forecasting these traffic flow
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc8783ef4a71fcaa0456319185d00a65
http://arxiv.org/abs/2106.06273
http://arxiv.org/abs/2106.06273
Publikováno v:
ICASSP
Hierarchy preserving network embedding is a method that project nodes into feature space by preserving the hierarchy property of networks. Recently, researches on network representation have considerably profited from taking hierarchy into considerat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::71486239f93cb0aab6e9e46e02e03172
https://doi.org/10.1109/icassp39728.2021.9414919
https://doi.org/10.1109/icassp39728.2021.9414919
Spatial-temporal forecasting has attracted tremendous attention in a wide range of applications, and traffic flow prediction is a canonical and typical example. The complex and long-range spatial-temporal correlations of traffic flow bring it to a mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::63bec37790983b404acfb88a481f857f
Publikováno v:
Neurocomputing. 259:76-84
Learning spatio-temporal dependency structure is meaningful to characterize causal or statistical relationships. In many real-world applications, dependency structure is often characterized by time-lag between variables. For example, traffic system a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::caaa8e28efc6399d1f52286f4e2a11cf
https://doi.org/10.1016/j.neucom.2016.06.084
https://doi.org/10.1016/j.neucom.2016.06.084