## Abstract

Similarity search problems in high-dimensional data arise in many areas of computer science such as data bases, image analysis, machine learning, and natural language processing. One of the most prominent problems is finding the k nearest neighbors of a data point q 2 Rd in a large set of data points S Rd, under same distance measure such as Euclidean distance. In contrast to lower

dimensional settings, we do not know of worst-case efficient data structures for such search problems in high-dimensional data, i.e., data structures that are faster than a linear scan through the data set. However, there is a rich body of (often heuristic) approaches that solve nearest neighbor search problems much faster than such a scan on many real-world data sets. As a necessity, the term solve

means that these approaches give approximate results that are close to the true k-nearest neighbors.

In this talk, we survey recent approaches to nearest neighbor search and related problems.

The talk consists of three parts: (1) What makes nearest neighbor search difficult? (2) How do

current state-of-the-art algorithms work? (3) What are recent advances regarding similarity search

on GPUs, in distributed settings, or in external memory?

dimensional settings, we do not know of worst-case efficient data structures for such search problems in high-dimensional data, i.e., data structures that are faster than a linear scan through the data set. However, there is a rich body of (often heuristic) approaches that solve nearest neighbor search problems much faster than such a scan on many real-world data sets. As a necessity, the term solve

means that these approaches give approximate results that are close to the true k-nearest neighbors.

In this talk, we survey recent approaches to nearest neighbor search and related problems.

The talk consists of three parts: (1) What makes nearest neighbor search difficult? (2) How do

current state-of-the-art algorithms work? (3) What are recent advances regarding similarity search

on GPUs, in distributed settings, or in external memory?

Original language | English |
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Title of host publication | 18th International Symposium on Experimental Algorithms (SEA 2020) |

Publisher | Schloss Dagstuhl--Leibniz-Zentrum für Informatik |

Publication date | 2020 |

Pages | 1:1–1:3 |

Article number | 1 |

DOIs | |

Publication status | Published - 2020 |

Event | International Symposium on Experimental Algorithms (SEA 2020) - Duration: 12 Jun 2020 → … Conference number: 18 |

### Conference

Conference | International Symposium on Experimental Algorithms (SEA 2020) |
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Number | 18 |

Period | 12/06/2020 → … |

Series | Leibniz International Proceedings in Informatics (LIPIcs) |
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ISSN | 1868-8969 |

## Keywords

- Similarity search
- High-dimensional data
- Nearest neighbor search
- Euclidean distance
- Approximate algorithms