Similarity Searching for the Big Data Challenges and Research Objectives

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F15%3A00087421" target="_blank" >RIV/00216224:14330/15:00087421 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11036-014-0547-2" target="_blank" >http://dx.doi.org/10.1007/s11036-014-0547-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11036-014-0547-2" target="_blank" >10.1007/s11036-014-0547-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Similarity Searching for the Big Data Challenges and Research Objectives

Popis výsledku v původním jazyce

Analysis of contemporary Big Data collections require an effective and efficient content-based access to data which is usually unstructured. This first implies a necessity to uncover descriptive knowledge of complex and heterogeneous objects to make themfindable. Second, multimodal search structures are needed to efficiently execute complex similarity queries possibly in outsourced environments while preserving privacy. After explaining the impacts of Big Data on similarity searching and summarizing the state of the art in the search technology, four specific research objectives to tackle the challenges are outlined and discussed. It is believed that effective and efficient processing of raw data for object findability and developing hybrid similaritysearch structures for multi-modal and privacy-preserving searching are necessary to achieve a scalable similarity search technology able to operate on Big Data.

Název v anglickém jazyce

Similarity Searching for the Big Data Challenges and Research Objectives

Popis výsledku anglicky

Analysis of contemporary Big Data collections require an effective and efficient content-based access to data which is usually unstructured. This first implies a necessity to uncover descriptive knowledge of complex and heterogeneous objects to make themfindable. Second, multimodal search structures are needed to efficiently execute complex similarity queries possibly in outsourced environments while preserving privacy. After explaining the impacts of Big Data on similarity searching and summarizing the state of the art in the search technology, four specific research objectives to tackle the challenges are outlined and discussed. It is believed that effective and efficient processing of raw data for object findability and developing hybrid similaritysearch structures for multi-modal and privacy-preserving searching are necessary to achieve a scalable similarity search technology able to operate on Big Data.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

MOBILE NETWORKS & APPLICATIONS

ISSN

1383-469X

e-ISSN

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Svazek periodika

20

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CN - Čínská lidová republika

Počet stran výsledku

10

Strana od-do

487-496

Kód UT WoS článku

000360003900010

EID výsledku v databázi Scopus

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