Multivariate Similarity Search - A Call for a New Breed of Similarity Search Algorithms

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.

Originele taal-2Engels
Titel2024 IEEE 40th International Conference on Data Engineering, ICDE 2024
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's5662
Aantal pagina's1
ISBN van elektronische versie979-8-3503-1715-2
DOI's
StatusGepubliceerd - 23 jul. 2024
Evenement40th IEEE International Conference on Data Engineering - Utrecht, Nederland
Duur: 13 mei 202417 mei 2024
Congresnummer: 40
https://icde2024.github.io/

Congres

Congres40th IEEE International Conference on Data Engineering
Verkorte titelICDE
Land/RegioNederland
StadUtrecht
Periode13/05/2417/05/24
Internet adres

Financiering

This work has received funding from the European Union's Horizon Europe programme STELAR under Grant Agreement No. 101070122.

FinanciersFinanciernummer
European Union’s Horizon Europe research and innovation programme101070122

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