High-dimensional similarity search for scalable data science


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Output type: Conference proceedings article

UM6P affiliated Publication?: Yes

Author list: Echihabi K., Zoumpatianos K., Palpanas T.

Publication year: 2021

Volume number: 2021-April

Start page: 2369

End page: 2372

Number of pages: 4

ISSN: 1084-4627

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109263982&doi=10.1109%2fICDE51399.2021.00268&partnerID=40&md5=101bd803bbd732acd7305a42a4442838

Languages: English (EN-GB)


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Abstract

Similarity search is a core operation of many critical data science applications, involving massive collections of high-dimensional objects. Similarity search finds objects in a collection close to a given query according to some definition of sameness. Objects can be data series, text, multimedia, graphs, database tables or deep network embeddings. In this tutorial, we revisit the similarity search problem in light of the recent advances in the field and the new big data landscape. We discuss key data science applications that require efficient high-dimensional similarity search, we survey the state-of-the-art high-dimensional similarity search approaches and share surprising insights about their strengths and weaknesses, and we discuss the challenges and open research problems in this area. © 2021 IEEE.


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Last updated on 2021-26-11 at 23:16