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Title	Uctb: An Urban Computing Tool Box For Building Spatiotemporal Prediction Services
ID_Doc	59339
Authors	Fang J.; Chen L.; Chai D.; Hong Y.; Xie X.; Chen L.; Wang L.
Year	2024
Published	Proceedings - 2024 IEEE International Conference on Software Services Engineering, SSE 2024
DOI	http://dx.doi.org/10.1109/SSE62657.2024.00020
Abstract	Spatiotemporal prediction (STP) service is one of the key infrastructure applications in smart cities. Currently, most of the existing STP services are constructed following the workflow of building deep learning (DL) applications while neglecting the importance of domain knowledge and region partition. However, the performance and interpretability of STP are highly related to them. As a result, there is an urgent requirement to develop a thorough and tailored workflow for STP services. To address this gap, we propose a novel workflow including two factors above as intermediate procedures. Based on the workflow, we design and implement an STP toolbox called UCTB (Urban Computing Tool Box) assisting practitioners in the rapid construction of STP services, which can manage multiple spatiotemporal do-main knowledge, support various region partition algorithms, and possess state-of-the-art models simultaneously. The relevant code and supporting documents have been open-sourced at https://github.com/uctb/UCIB. © 2024 IEEE.
Author Keywords	software service; spatiotemporal prediction; toolbox; workflow

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