Abstract
Conversational agent has become a new way of conducting data analysis tasks, enabling people with different levels of analytic experience to interact with the system by providing natural language (NL) commands, choices, and parameters etc. However, flexibility becomes a challenge considering that users may want to integrate model services on the Web in such systems. To address it, we present Flowris, a prototype system that collects and manages provenance data for conversational agent. We will show how Flowris collects and manages provenance data, and how it supports the comparison and reproducibility of experiments.
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Acknowledgements
This work is supported by National Key Research and Development Program (No. 2020YFB1710004) and the National Science Foundation of China under the grant 62272466.
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Sun, J., Wang, J., Chen, Y., Qin, X. (2023). Flowris: Managing Data Analysis Workflows for Conversational Agent. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13946. Springer, Cham. https://doi.org/10.1007/978-3-031-30678-5_63
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DOI: https://doi.org/10.1007/978-3-031-30678-5_63
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