On the Similarity Search With Hamming Space Sketches

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00121403" target="_blank" >RIV/00216224:14330/21:00121403 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.4018/978-1-7998-4963-6.ch005" target="_blank" >http://dx.doi.org/10.4018/978-1-7998-4963-6.ch005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/978-1-7998-4963-6.ch005" target="_blank" >10.4018/978-1-7998-4963-6.ch005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the Similarity Search With Hamming Space Sketches

Popis výsledku v původním jazyce

This chapter focuses on data searching, which is nowadays mostly based on similarity. The similarity search is challenging due to its computational complexity, and also the fact that similarity is subjective and context dependent. The authors assume the metric space model of similarity, defined by the domain of objects and the metric function that measures the dissimilarity of object pairs. The volume of contemporary data is large, and the time efficiency of similarity query executions is essential. This chapter investigates transformations of metric space to Hamming space to decrease the memory and computational complexity of the search. Various challenges of the similarity search with sketches in the Hamming space are addressed, including the definition of sketching transformation and efficient search algorithms that exploit sketches to speed-up searching. The indexing of Hamming space and a heuristic to facilitate the selection of a suitable sketching technique for any given application are also considered.

Název v anglickém jazyce

On the Similarity Search With Hamming Space Sketches

Popis výsledku anglicky

This chapter focuses on data searching, which is nowadays mostly based on similarity. The similarity search is challenging due to its computational complexity, and also the fact that similarity is subjective and context dependent. The authors assume the metric space model of similarity, defined by the domain of objects and the metric function that measures the dissimilarity of object pairs. The volume of contemporary data is large, and the time efficiency of similarity query executions is essential. This chapter investigates transformations of metric space to Hamming space to decrease the memory and computational complexity of the search. Various challenges of the similarity search with sketches in the Hamming space are addressed, including the definition of sketching transformation and efficient search algorithms that exploit sketches to speed-up searching. The indexing of Hamming space and a heuristic to facilitate the selection of a suitable sketching technique for any given application are also considered.

Druh

C - Kapitola v odborné knize

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

2021

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Intelligent Analytics With Advanced Multi-Industry Applications

ISBN

9781799849636

Počet stran výsledku

31

Strana od-do

97-127

Počet stran knihy

392

Název nakladatele

IGI Global

Místo vydání

Hershey, PA (USA)

Kód UT WoS kapitoly

—