Filtering with relational similarity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00139281" target="_blank" >RIV/00216224:14330/24:00139281 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.is.2024.102345" target="_blank" >https://doi.org/10.1016/j.is.2024.102345</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.is.2024.102345" target="_blank" >10.1016/j.is.2024.102345</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Filtering with relational similarity

Popis výsledku v původním jazyce

For decades, the success of the similarity search has been based on detailed quantifications of pairwise similarities of objects. Currently, the search features have become much more precise but also bulkier, and the similarity computations are more time-consuming. We show that nearly no precise similarity quantifications are needed to evaluate the k nearest neighbours (kNN) queries that dominate real -life applications. Based on the well-known fact that a selection of the most similar alternative out of several options is a much easier task than deciding the absolute similarity scores, we propose the search based on an epistemologically simpler concept of relational similarity. Having arbitrary objects q, o1, o2 from the search domain, the kNN search is solvable just by the ability to choose the more similar object to q out of o1, o2. To support the filtering efficiency, we also consider a neutral option, i.e., equal similarities of q, o1 and q, o2. We formalise such concept and discuss its advantages with respect to similarity quantifications, namely the efficiency, robustness and scalability with respect to the dataset size. Our pioneering implementation of the relational similarity search for the Euclidean and Cosine spaces demonstrates robust filtering power and efficiency compared to several contemporary techniques.

Název v anglickém jazyce

Filtering with relational similarity

Popis výsledku anglicky

For decades, the success of the similarity search has been based on detailed quantifications of pairwise similarities of objects. Currently, the search features have become much more precise but also bulkier, and the similarity computations are more time-consuming. We show that nearly no precise similarity quantifications are needed to evaluate the k nearest neighbours (kNN) queries that dominate real -life applications. Based on the well-known fact that a selection of the most similar alternative out of several options is a much easier task than deciding the absolute similarity scores, we propose the search based on an epistemologically simpler concept of relational similarity. Having arbitrary objects q, o1, o2 from the search domain, the kNN search is solvable just by the ability to choose the more similar object to q out of o1, o2. To support the filtering efficiency, we also consider a neutral option, i.e., equal similarities of q, o1 and q, o2. We formalise such concept and discuss its advantages with respect to similarity quantifications, namely the efficiency, robustness and scalability with respect to the dataset size. Our pioneering implementation of the relational similarity search for the Euclidean and Cosine spaces demonstrates robust filtering power and efficiency compared to several contemporary techniques.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Information systems

ISSN

0306-4379

e-ISSN

0306-4379

Svazek periodika

122

Číslo periodika v rámci svazku

102345

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

001173066400001

EID výsledku v databázi Scopus

2-s2.0-85184992306