Towards Faster Similarity Search by Dynamic Reordering of Streamed Queries

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00101119" target="_blank" >RIV/00216224:14330/18:00101119 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-662-58384-5_3" target="_blank" >http://dx.doi.org/10.1007/978-3-662-58384-5_3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-662-58384-5_3" target="_blank" >10.1007/978-3-662-58384-5_3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Faster Similarity Search by Dynamic Reordering of Streamed Queries

Popis výsledku v původním jazyce

Current era of digital data explosion calls for employment of content-based similarity search techniques, since traditional searchable metadata like annotations are not always available. In our work, we focus on a scenario where the similarity search is used in the context of stream processing, which is one of the suitable approaches to deal with huge amounts of data. Our goal is to maximize the throughput of processed queries while a slight delay is acceptable. We propose a technique that dynamically reorders the queries coming from the stream in order to use our caching mechanism in huge data spaces more effectively. We were able to achieve significantly higher throughput compared to the baseline when no reordering and no caching were used. Moreover, our proposal does not incur any additional precision loss of the similarity search, as opposed to some other caching techniques. In addition to the throughput maximization, we also study the potential of trading off the throughput for low delays (waiting times). The proposed technique allows to be parameterized by the amount of the throughput that can be sacrificed.

Název v anglickém jazyce

Towards Faster Similarity Search by Dynamic Reordering of Streamed Queries

Popis výsledku anglicky

Current era of digital data explosion calls for employment of content-based similarity search techniques, since traditional searchable metadata like annotations are not always available. In our work, we focus on a scenario where the similarity search is used in the context of stream processing, which is one of the suitable approaches to deal with huge amounts of data. Our goal is to maximize the throughput of processed queries while a slight delay is acceptable. We propose a technique that dynamically reorders the queries coming from the stream in order to use our caching mechanism in huge data spaces more effectively. We were able to achieve significantly higher throughput compared to the baseline when no reordering and no caching were used. Moreover, our proposal does not incur any additional precision loss of the similarity search, as opposed to some other caching techniques. In addition to the throughput maximization, we also study the potential of trading off the throughput for low delays (waiting times). The proposed technique allows to be parameterized by the amount of the throughput that can be sacrificed.

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/GA16-18889S" target="_blank" >GA16-18889S: Analytika pro velká nestrukturovaná data</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII

ISBN

9783662583838

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

28

Strana od-do

61-88

Název nakladatele

Springer

Místo vydání

Berlin, Heidelberg

Místo konání akce

Berlin, Heidelberg

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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