Scalable polyadic queries

Richard Connor; Alan Dearle; Ben Claydon

doi:10.1007/978-3-031-75823-2_5

Scalable polyadic queries

Richard Connor, Alan Dearle, Ben Claydon

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In previous work, the notion of polyadic similarity query was introduced. Normally, similarity queries take a single argument and attempt to find those objects within a large collection which are most similar to that argument. The idea of polyadic queries is to generalise this notion, by taking a number of query arguments, and giving results based on some combination of their characteristics. It was previously shown how polyadic queries could be of use in various contexts.

The initial work on polyadic queries provided a proof of concept but left many unanswered questions. In particular, it did not show a proper semantic basis for the polyadic query function used or how to achieve sub-linear query times for polyadic searches over large data.

Here, we address these issues. This work demonstrates that the polyadic query mechanism can scale to large data, and gives results which are better than those obtained by executing simple queries over each of the arguments individually.

Original language	English
Title of host publication	Similarity search and applications
Subtitle of host publication	17th International conference, SISAP 2024, Providence, RI, USA, November 4–6, 2024, proceedings
Editors	Edgar Chávez, Benjamin Kimia, Jakub Lokoč, Marco Patella, Jan Sedmidubsky
Place of Publication	Cham
Publisher	Springer
Pages	57-64
ISBN (Electronic)	9783031758232
ISBN (Print)	9783031758225
DOIs	https://doi.org/10.1007/978-3-031-75823-2_5
Publication status	Published - 25 Oct 2024
Event	17th International Conference on Similarity Search and Applications, SISAP 2024 - Providence, United States Duration: 4 Nov 2024 → 6 Nov 2024 Conference number: 17 https://www.sisap.org/2024/

Publication series

Name	Lecture notes in computer science
Publisher	Springer
Volume	15268
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Similarity Search and Applications, SISAP 2024
Abbreviated title	SISAP 2024
Country/Territory	United States
City	Providence
Period	4/11/24 → 6/11/24
Internet address	https://www.sisap.org/2024/

Keywords

Similarity search
Polyadic query
SED
MSED

Access to Document

10.1007/978-3-031-75823-2_5

Scalable polyadic queries (code)
Claydon, B. (Creator) & Dearle, A. (Creator), GitHub, 2025
https://github.com/MetricSearch/SISAP2024PolyadicQuery
Dataset: Software

Cite this

Connor, R., Dearle, A., & Claydon, B. (2024). Scalable polyadic queries. In E. Chávez, B. Kimia, J. Lokoč, M. Patella, & J. Sedmidubsky (Eds.), Similarity search and applications: 17th International conference, SISAP 2024, Providence, RI, USA, November 4–6, 2024, proceedings (pp. 57-64). (Lecture notes in computer science; Vol. 15268). Springer. https://doi.org/10.1007/978-3-031-75823-2_5

Connor, Richard ; Dearle, Alan ; Claydon, Ben. / Scalable polyadic queries. Similarity search and applications: 17th International conference, SISAP 2024, Providence, RI, USA, November 4–6, 2024, proceedings. editor / Edgar Chávez ; Benjamin Kimia ; Jakub Lokoč ; Marco Patella ; Jan Sedmidubsky. Cham : Springer, 2024. pp. 57-64 (Lecture notes in computer science).

@inproceedings{a853ad9f92e343c0b6d5c37ebc3c3736,

title = "Scalable polyadic queries",

abstract = "In previous work, the notion of polyadic similarity query was introduced. Normally, similarity queries take a single argument and attempt to find those objects within a large collection which are most similar to that argument. The idea of polyadic queries is to generalise this notion, by taking a number of query arguments, and giving results based on some combination of their characteristics. It was previously shown how polyadic queries could be of use in various contexts.The initial work on polyadic queries provided a proof of concept but left many unanswered questions. In particular, it did not show a proper semantic basis for the polyadic query function used or how to achieve sub-linear query times for polyadic searches over large data.Here, we address these issues. This work demonstrates that the polyadic query mechanism can scale to large data, and gives results which are better than those obtained by executing simple queries over each of the arguments individually.",

keywords = "Similarity search, Polyadic query, SED, MSED",

author = "Richard Connor and Alan Dearle and Ben Claydon",

year = "2024",

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doi = "10.1007/978-3-031-75823-2_5",

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isbn = "9783031758225",

series = "Lecture notes in computer science",

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editor = "Edgar Ch{\'a}vez and Benjamin Kimia and Jakub Loko{\v c} and Marco Patella and Jan Sedmidubsky",

booktitle = "Similarity search and applications",

address = "Netherlands",

note = "17th International Conference on Similarity Search and Applications, SISAP 2024, SISAP 2024 ; Conference date: 04-11-2024 Through 06-11-2024",

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Connor, R , Dearle, A & Claydon, B 2024, Scalable polyadic queries. in E Chávez, B Kimia, J Lokoč, M Patella & J Sedmidubsky (eds), Similarity search and applications: 17th International conference, SISAP 2024, Providence, RI, USA, November 4–6, 2024, proceedings. Lecture notes in computer science, vol. 15268, Springer, Cham, pp. 57-64, 17th International Conference on Similarity Search and Applications, SISAP 2024, Providence, Rhode Island, United States, 4/11/24. https://doi.org/10.1007/978-3-031-75823-2_5

Scalable polyadic queries. / Connor, Richard ; Dearle, Alan; Claydon, Ben.
Similarity search and applications: 17th International conference, SISAP 2024, Providence, RI, USA, November 4–6, 2024, proceedings. ed. / Edgar Chávez; Benjamin Kimia; Jakub Lokoč; Marco Patella; Jan Sedmidubsky. Cham: Springer, 2024. p. 57-64 (Lecture notes in computer science; Vol. 15268).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Scalable polyadic queries

AU - Connor, Richard

AU - Dearle, Alan

AU - Claydon, Ben

N1 - Conference code: 17

PY - 2024/10/25

Y1 - 2024/10/25

N2 - In previous work, the notion of polyadic similarity query was introduced. Normally, similarity queries take a single argument and attempt to find those objects within a large collection which are most similar to that argument. The idea of polyadic queries is to generalise this notion, by taking a number of query arguments, and giving results based on some combination of their characteristics. It was previously shown how polyadic queries could be of use in various contexts.The initial work on polyadic queries provided a proof of concept but left many unanswered questions. In particular, it did not show a proper semantic basis for the polyadic query function used or how to achieve sub-linear query times for polyadic searches over large data.Here, we address these issues. This work demonstrates that the polyadic query mechanism can scale to large data, and gives results which are better than those obtained by executing simple queries over each of the arguments individually.

AB - In previous work, the notion of polyadic similarity query was introduced. Normally, similarity queries take a single argument and attempt to find those objects within a large collection which are most similar to that argument. The idea of polyadic queries is to generalise this notion, by taking a number of query arguments, and giving results based on some combination of their characteristics. It was previously shown how polyadic queries could be of use in various contexts.The initial work on polyadic queries provided a proof of concept but left many unanswered questions. In particular, it did not show a proper semantic basis for the polyadic query function used or how to achieve sub-linear query times for polyadic searches over large data.Here, we address these issues. This work demonstrates that the polyadic query mechanism can scale to large data, and gives results which are better than those obtained by executing simple queries over each of the arguments individually.

KW - Similarity search

KW - Polyadic query

KW - SED

KW - MSED

U2 - 10.1007/978-3-031-75823-2_5

DO - 10.1007/978-3-031-75823-2_5

M3 - Conference contribution

SN - 9783031758225

T3 - Lecture notes in computer science

SP - 57

EP - 64

BT - Similarity search and applications

A2 - Chávez, Edgar

A2 - Kimia, Benjamin

A2 - Lokoč, Jakub

A2 - Patella, Marco

A2 - Sedmidubsky, Jan

PB - Springer

CY - Cham

T2 - 17th International Conference on Similarity Search and Applications, SISAP 2024

Y2 - 4 November 2024 through 6 November 2024

ER -

Scalable polyadic queries

Abstract

Publication series

Conference

Keywords

Access to Document

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Datasets

Scalable polyadic queries (code)

Cite this