Abstract
The similarity search task involves identifying pairs of similar vectors, e.g., time series. For example, given a query q, the user might wish to find all vectors in a dataset with a cosine similarity with q higher than a threshold t, or to find the top-k most similar vectors with q, using Euclidean distance. The task has been widely considered in different domains, ranging from data science for detecting correlations that help the analyst extract insights from the data, to e-commerce for recommending additional purchases to the users based on their shopping behavior. Accordingly, many similarity search algorithms and indices were proposed in the literature, focusing on efficiency, scalability for big datasets, and different distance measures. However, the majority of past work only considers pairwise similarity/distance measures. In this talk we will revisit similarity search under the lens of multivariate similarity measures.
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024 |
| Pages | 5662 |
| Number of pages | 1 |
| ISBN (Electronic) | 9798350317152 |
| DOIs | |
| Publication status | Published - 2024 |
Bibliographical note
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.Funding
This work has received funding from the European Union's Horizon Europe programme STELAR under Grant Agreement No. 101070122.
| Funders | Funder number |
|---|---|
| European Union's Horizon 2020 - Research and Innovation Framework Programme | 101070122 |
Keywords
- multivariate similarity measures