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However, the LBF has a massive time cost and does not apply to multidimensional spatial data. In this paper, we propose a prefix\u2010based and adaptive learned bloom filter (PA\u2010LBF) for spatial data, which efficiently supports the insertion and deletion. The proposed PA\u2010LBF is divided into three parts: (1) the prefix\u2010based classification. The <jats:italic>Z<\/jats:italic>\u2010order space\u2010filling curve is used to extract data, prefix it, and classify it. (2) The adaptive learning process. The multiple independent adaptive sub\u2010LBFs are designed to train the suffixes of data, combined with part 1, to reduce the false positive rate (FPR), query, and learning process time consumption. (3) The backup filter uses CBF. Two kinds of backup CBF are constructed to meet the situation of different insertion and deletion frequencies. Experimental results prove the validity of the theory and show that the PA\u2010LBF reduces the FPR by 84.87%, 79.53%, and 43.01% with the same memory usage compared with the LBF on three real\u2010world spatial datasets. Moreover, the time consumption of PA\u2010LBF can be reduced to 5\u00d7 and 2.05\u00d7 that of the LBF on the query and learning process, respectively.<\/jats:p>","DOI":"10.1155\/2023\/4970776","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T03:35:06Z","timestamp":1680147306000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["PA\u2010LBF: Prefix\u2010Based and Adaptive Learned Bloom Filter for Spatial Data"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6316-8765","authenticated-orcid":false,"given":"Meng","family":"Zeng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3542-1097","authenticated-orcid":false,"given":"Beiji","family":"Zou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9957-7867","authenticated-orcid":false,"given":"Xiaoyan","family":"Kui","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8825-0992","authenticated-orcid":false,"given":"Chengzhang","family":"Zhu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2576-2963","authenticated-orcid":false,"given":"Ling","family":"Xiao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2911-5234","authenticated-orcid":false,"given":"Zhi","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6771-7407","authenticated-orcid":false,"given":"Jingyu","family":"Du","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"e_1_2_13_1_2","doi-asserted-by":"publisher","DOI":"10.1145\/362686.362692"},{"key":"e_1_2_13_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22807"},{"key":"e_1_2_13_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/tc.2015.2444850"},{"key":"e_1_2_13_4_2","first-page":"11700","article-title":"Adaptive learned bloom filter (ada-bf): efficient utilization of the classifier with application to real-time information filtering on the web","volume":"33","author":"Dai Z.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_13_5_2","unstructured":"PatgiriR. 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