Similarity Search with the Distance Density Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00127335" target="_blank" >RIV/00216224:14330/22:00127335 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-17849-8_10" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-17849-8_10</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-17849-8_10" target="_blank" >10.1007/978-3-031-17849-8_10</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Similarity Search with the Distance Density Model

Popis výsledku v původním jazyce

The metric space model of similarity has become a standard formal paradigm of generic similarity search engine implementations. However, the constraints of identity and symmetry prevent from expressing the subjectivity and dependence on the context perceived by humans. In this paper, we study the suitability of the Distance density model of similarity for searching. First, we use the Local Outlier Factor (LOF) to estimate a data density in search collections and evaluate plenty of queries using the standard geometric model and its extension respecting the densities. We let 200 people assess the search effectiveness of the two alternatives using the web interface. Encouraged by the positive effects of the Distance density model, we propose an alternative way to estimate the data densities to avoid the quadratic LOF computation complexity with respect to the dataset size. The sketches with unbalanced bits are clarified to be in correlation with LOFs, which opens a possibility for an efficient implementation of large-scale similarity search systems based on the Distance density model.

Název v anglickém jazyce

Similarity Search with the Distance Density Model

Popis výsledku anglicky

The metric space model of similarity has become a standard formal paradigm of generic similarity search engine implementations. However, the constraints of identity and symmetry prevent from expressing the subjectivity and dependence on the context perceived by humans. In this paper, we study the suitability of the Distance density model of similarity for searching. First, we use the Local Outlier Factor (LOF) to estimate a data density in search collections and evaluate plenty of queries using the standard geometric model and its extension respecting the densities. We let 200 people assess the search effectiveness of the two alternatives using the web interface. Encouraged by the positive effects of the Distance density model, we propose an alternative way to estimate the data densities to avoid the quadratic LOF computation complexity with respect to the dataset size. The sketches with unbalanced bits are clarified to be in correlation with LOFs, which opens a possibility for an efficient implementation of large-scale similarity search systems based on the Distance density model.

Druh

D - Stať ve sborníku

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

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Similarity Search and Applications: 15th International Conference, SISAP 2022, Bologna, Italy, October 5 - October 7, 2020, Proceedings

ISBN

9783031178481

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

15

Strana od-do

118-132

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Bologna, Itálie

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000874756300010