Survey on Exact kNN Queries over High-Dimensional Data Space

RAIDS Lab Authors

Zhengyi Yang

(corresponding author)

Director

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Abstract

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from data mining, recommendation system and Internet of Things, to Industry 4.0 framework applications. In mining, specifically, it can be used for the classification of human activities, iterative closest point registration and pattern recognition and has also been helpful for intrusion detection systems and fault detection. Due to the importance of kNN queries, many algorithms have been proposed in the literature, for both static and dynamic data. In this paper, we focus on exact kNN queries and present a comprehensive survey of exact kNN queries. In particular, we study two fundamental types of exact kNN queries: the kNN Search queries and the kNN Join queries. Our survey focuses on exact approaches over high-dimensional data space, which covers 20 kNN Search methods and 9 kNN Join methods. To the best of our knowledge, this is the first work of a comprehensive survey of exact kNN queries over high-dimensional datasets. We specifically categorise the algorithms based on indexing strategies, data and space partitioning strategies, clustering techniques and the computing paradigm. We provide useful insights for the evolution of approaches based on the various categorisation factors, as well as the possibility of further expansion. Lastly, we discuss some open challenges and future research directions.

Author Affiliations

@article{ukey2023survey,
  title = {Survey on Exact kNN Queries over High-Dimensional Data Space},
  author = {Ukey, Nimish and Yang, Zhengyi and Li, Binghao and Zhang, Guangjian and Hu, Yiheng and Zhang, Wenjie},
  volume = {23},
  issn = {1424-8220},
  url = {http://dx.doi.org/10.3390/s23020629},
  doi = {10.3390/s23020629},
  number = {2},
  journal = {Sensors},
  publisher = {MDPI AG},
  year = {2023},
  month = Jan,
  pages = {629}
}