Abstract
Semantic knowledge mapping is widely used in search, question answering and analysis scenarios, which needs scalable storage mode and distributed parallel query support. In this paper, we design a distributed aggregate storage model with the characteristics of balanced storage load distribution and local node aggregate storage under the big table model, and a distributed parallel query engine with distributed parallel computing query tree based on group by model. Experimental results show that the storage mode and query engine designed in this paper have good horizontal scalability.
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Chen, Y., Xue, W., Wang, L., Li, Y., Xing, X. (2022). Design of Big Data Management and Analysis Platform Based on Knowledge Map. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 102. Springer, Singapore. https://doi.org/10.1007/978-981-16-7466-2_67
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DOI: https://doi.org/10.1007/978-981-16-7466-2_67
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