Abstract
Learned bloom filter (LBF) model has been proposed in recent work to replace the traditional bloom filter (BF). It can reduce the needed amount of memory and achieve a relatively low false positive rate (FPR). However, the LBF did not provide a good solution for multi-dimensional data, such as spatial data. In this paper, a learned prefix bloom filter (LPBF) for spatial data is presented, which supports deletion and expansion and achieves lower FPR and less memory usage than the classical BF. To our knowledge, this is the first LBF method for spatial data. Specifically, a Z-order space-filling curve is used to map the spatial data into one dimension binary code. Then, we only need to learn the suffixes of the same prefix for the corresponding sub-LBF, which reduces the learning complexity of LBF. We further use the perfect hash table to accelerate the filter and reduce the FPR. Compared with two traditional BF methods and two state-of-art LBF methods on real spatial data sets, the proposed LPBF method shows the best performance in reducing FPR, proving that the LPBF method has great potential on bloom filter for spatial data.
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Acknowledgements
This work is supported in part by the National Key R &D Program of China (2018AAA0102100), the Scientific and Technological Innovation Leading Plan of High-tech Industry of Hunan Province (2020GK2021), the National Natural Science Foundation of China (61902434), the International Science and Technology Innovation Joint Base of Machine Vision and Medical Image Processing in Hunan Province (2021CB1013).
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Zou, B., Zeng, M., Zhu, C., Xiao, L., Chen, Z. (2022). A Learned Prefix Bloom Filter for Spatial Data. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_26
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