Abstract
In medical domain, huge amounts of data are generated at all times. These data are usually difficult to access, with poor data quality and many data islands. Besides, with a wide range of sources and complex structure, these data contain essential information and are difficult to manage. However, few existing data management frameworks based on Data Lake excel in solving the persistence and the analysis efficiency for medical multi-source heterogeneous data. In this paper, we propose an efficient Multi-source Heterogeneous Data Lake Platform (MHDP) to realize the efficient medical data management. Firstly, we propose an efficient and unified method based on Data Lake to store data of different types and different sources persistently. Secondly, based on the unified data store, an efficient multi-source heterogeneous data fusion is implemented to effectively manage data. Finally, an efficient data query strategy is carried out to assist doctors in medical decision-making. In-depth analysis on applications shows that MHDP delivers better performance for data management in medical domain.
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Acknowledgements
This work was supported by National Key R&D Program of China (2019YFC0119600).
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Ren, P. et al. (2021). MHDP: An Efficient Data Lake Platform for Medical Multi-source Heterogeneous Data. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_63
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DOI: https://doi.org/10.1007/978-3-030-87571-8_63
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