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.
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 |
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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 | |
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 |
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Publisher | Springer |
Volume | 15268 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 17th International Conference on Similarity Search and Applications, SISAP 2024 |
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Abbreviated title | SISAP 2024 |
Country/Territory | United States |
City | Providence |
Period | 4/11/24 → 6/11/24 |
Internet address |
Keywords
- Similarity search
- Polyadic query
- SED
- MSED
Fingerprint
Dive into the research topics of 'Scalable polyadic queries'. Together they form a unique fingerprint.Datasets
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Scalable polyadic queries (code)
Claydon, B. (Creator) & Dearle, A. (Creator), GitHub, 2025
https://github.com/MetricSearch/SISAP2024PolyadicQuery
Dataset: Software