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Your search keyword '"He, Huajun"' showing total 24 results
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3. Outlook and Conclusions

Author

Tay, Yong Kang Eugene, He, Huajun, Tian, Xiangling, Li, Mingjie, Sum, Tze Chien, Tay, Yong Kang Eugene, He, Huajun, Tian, Xiangling, Li, Mingjie, and Sum, Tze Chien

Published
2022
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4. The Halide Perovskite Gain Media

Author

Tay, Yong Kang Eugene, He, Huajun, Tian, Xiangling, Li, Mingjie, Sum, Tze Chien, Tay, Yong Kang Eugene, He, Huajun, Tian, Xiangling, Li, Mingjie, and Sum, Tze Chien

Published
2022
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6. An Efficient System for Large-Scale Collaborative Tasks in Government.

Author

Wang, Lulu, He, Huajun, Pan, Zheyi, Bao, Jie, and Zheng, Yu

Subjects
GRASSROOTS movements, STATISTICS, BUSINESS models, ORGANIZATIONAL structure, BUREAUCRATIZATION
Abstract

Government collaboration tasks are integral for grassroots governance and essential for government administration. Large-scale government collaboration tasks often involve multiple departments working together to solve complex tasks that require handling large amounts of data. However, existing offline processes make it difficult to manage complex collaboration scenarios and obtain timely execution results. We introduce the concept of a spatio-temporal task tree and propose a log-based incremental update method for updating statistical values in the tree structure to fulfill the need for real-time aggregation analysis of cross-temporal task execution situations. The spatio-temporal task tree fosters collaboration across various departments and organizational hierarchies to execute complex and large-scale tasks in government effectively. By flexible adaptations to different business models, this framework addresses the shortcomings of conventional systems in promoting inter-organizational collaboration. The log-based incremental update method for calculating statistical values within a tree structure allows for the updating of multi-dimensional data statistics in seconds. This approach effectively decreases computational expenses, enhances query efficiency, and improves real-time computation response. Results from experiments demonstrate the feasibility and high availability of the proposed approach, showcasing their potential to address the challenges associated with large-scale collaborative tasks in government affairs. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Weakly Confined Organic–Inorganic Halide Perovskite Quantum Dots as High-Purity Room-Temperature Single Photon Sources

Author

Wang, Bo, primary, Lim, Jia Wei Melvin, additional, Loh, Siow Mean, additional, Mayengbam, Rishikanta, additional, Ye, Senyun, additional, Feng, Minjun, additional, He, Huajun, additional, Liang, Xiao, additional, Cai, Rui, additional, Zhang, Qiannan, additional, Kwek, Leong-Chuan, additional, Demir, Hilmi Volkan, additional, Mhaisalkar, Subodh G., additional, Blundell, Steven A., additional, and Chien Sum, Tze, additional

Published
2024
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10. Outlook and Conclusions

Author

Tay, Yong Kang Eugene, primary, He, Huajun, additional, Tian, Xiangling, additional, Li, Mingjie, additional, and Sum, Tze Chien, additional

Published
2022
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11. Halide Perovskite Lasers

Author

Tay, Yong Kang Eugene, primary, He, Huajun, additional, Tian, Xiangling, additional, Li, Mingjie, additional, and Sum, Tze Chien, additional

Published
2022
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14. Epidemic Amplifier Detection: Finding High-Risk Locations in COVID-19 Cases' Location Sequences via Multi-task Learning

Author

Hu, Yue, primary, Wang, Xiaoli, additional, Ren, Huimin, additional, He, Tianfu, additional, Tang, Tan, additional, He, Huajun, additional, Meng, Chuishi, additional, Han, Boyang, additional, Bao, Jie, additional, Sun, Ying, additional, Li, Gang, additional, Wang, Quanyi, additional, Yang, Peng, additional, and Zheng, Yu, additional

Published
2023
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19. POI Alias Discovery in Delivery Addresses using User Locations

Author

He, Tianfu, primary, Chen, Guochun, additional, Meng, Chuishi, additional, He, Huajun, additional, Pan, Zheyi, additional, Li, Yexin, additional, Ruan, Sijie, additional, Ren, Huimin, additional, Yuan, Ye, additional, Li, Ruiyuan, additional, Zhang, Junbo, additional, Bao, Jie, additional, He, Hui, additional, and Zheng, Yu, additional

Published
2021
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20. SALON

Author

Hu, Yue, primary, Ruan, Sijie, additional, Ni, Yuting, additional, He, Huajun, additional, Bao, Jie, additional, Li, Ruiyuan, additional, and Zheng, Yu, additional

Published
2021
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21. Distributed Spatio-Temporal k Nearest Neighbors Join

Author

Li, Ruiyuan, primary, Wang, Rubin, additional, Liu, Junwen, additional, Yu, Zisheng, additional, He, Huajun, additional, He, Tianfu, additional, Ruan, Sijie, additional, Bao, Jie, additional, Chen, Chao, additional, Gu, Fuqiang, additional, Hong, Liang, additional, and Zheng, Yu, additional

Published
2021
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22. JUST-Traj

Author

He, Huajun, primary, Li, Ruiyuan, additional, Bao, Jie, additional, Li, Tianrui, additional, and Zheng, Yu, additional

Published
2021
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23. TrajMesa: A Distributed NoSQL-Based Trajectory Data Management System

Author

Li, Ruiyuan, He, Huajun, Wang, Rubin, Ruan, Sijie, He, Tianfu, Bao, Jie, Zhang, Junbo, Hong, Liang, and Zheng, Yu

Abstract

With the development of positioning technology, a large number of trajectories have been generated, which are very useful for many urban applications. However, it is challenging to manage trajectory data for its spatio-temporal dynamics and high-volume properties. Existing trajectory data management frameworks suffer from efficiency or scalability problem, and support only limited trajectory query types. This paper takes the first attempt to build a holistic distributed NoSQL trajectory query engine, named TrajMesa, based on GeoMesa, an open-source indexing toolkit for spatio-temporal data. TrajMesa can manage a prohibitively large number of trajectories, and support plenty of query types efficiently. Specifically, we first design a novel trajectory storage schema, which reduces the storage size tremendously. We then devise a novel indexing key schema for time ranges, based on which ID (i.e., moving object identifier) temporal query can be supported efficiently. To reduce the amount of retrieved trajectory data for a spatial range query, we propose a position code to indicate the spatial location of trajectories accurately. We also propose a bunch of pruning strategies for similarity query and $k$k-NN query in the NoSQL environment. Extensive experiments are conducted using two real datasets and one synthetic dataset, verifying the powerful query efficiency and scalability of TrajMesa. The results show that TrajMesa is about $100 \sim 1000$100∼1000 times faster than the state-of-the-art trajectory management frameworks in our experimental settings. TrajMesa is currently deployed in JD company, processing over 1T trajectories of JD Logistics every day.

Published
2023
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