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Towards Faster Similarity Search by Dynamic Reordering of Streamed Queries

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Towards Faster Similarity Search by Dynamic Reordering of Streamed Queries

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

    <a href="/en/project/GA16-18889S" target="_blank" >GA16-18889S: Big Data Analytics for Unstructured Data</a><br>

  • Continuities

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

Others

  • Publication year

    2018

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

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

  • ISBN

    9783662583838

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    28

  • Pages from-to

    61-88

  • Publisher name

    Springer

  • Place of publication

    Berlin, Heidelberg

  • Event location

    Berlin, Heidelberg

  • Event date

    Jan 1, 2018

  • Type of event by nationality

    CST - Celostátní akce

  • UT code for WoS article