LSR-Forest: An LSH-Based Approximate k-Nearest Neighbor Query Algorithm on High-Dimensional Uncertain Data

Wang, Jia-Gang; Qian, Tao; Yang, An‐Gang; Wang, Hui; Qian, Jiangbo

doi:10.1007/978-981-15-2810-1_17

Cited by 2 publications

(1 citation statement)

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“…The above variables and parameters could be considered as attributes of an uncertain data, as their values may have some uncertainty due to device instability, transmission delay, power failure, or many other reasons. Furthermore, some databases for special needs, such as privacy protection, it is necessary to generalize some sensitive dimensions of accurate data 1-5 . In the above scenarios, uncertain data is more suitable for describing data objects than accurate data 6-8 .…”

Section: Introductionmentioning

confidence: 99%

LSR‐forest: An locality sensitive hashing‐based approximate k‐nearest neighbor query algorithm on high‐dimensional uncertain data

Wang

Qian

Yang

et al. 2020

Concurrency and Computation

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Summary Uncertain data is widely used in many practical applications, such as data cleaning, location‐based services, privacy protection, and so on. With the development of technology, data has a tendency to high‐dimensionality. The most common indexes for nearest neighbor search on uncertain data are the R‐Tree and the KD‐Tree. These indexes will inevitably bring about “curse of dimension.” Focus on this problem, article proposes a new hash algorithm, called the LSR‐forest, which based on locality sensitive hashing and R‐Tree, to solve the high‐dimensional uncertain data approximate neighbor search problem. The LSR‐forest can hash similar high‐dimensional uncertain data into a same bucket with a high probability, and then constructs multiple R‐Tree‐based indexes for hashed buckets. When querying, it is possible to judge neighbors by checking the data in the hypercube which the query point is in. One can also adjust the query range automatically by different parameter of k. Many experiments on different datasets are presented in this article. The results show that LSR‐forest has better effectiveness and efficiency than R‐Tree on high‐dimensional datasets.

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Section: Introductionmentioning

confidence: 99%