Comparing MapReduce-Based k-NN Similarity Joins On Hadoop For High-dimensional Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10366491" target="_blank" >RIV/00216208:11320/17:10366491 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-69179-4_5" target="_blank" >http://dx.doi.org/10.1007/978-3-319-69179-4_5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-69179-4_5" target="_blank" >10.1007/978-3-319-69179-4_5</a>

Result language
angličtina
Original language name
Comparing MapReduce-Based k-NN Similarity Joins On Hadoop For High-dimensional Data
Original language description
Similarity joins represent a useful operator for data mining, data analysis and data exploration applications. With the exponential growth of data to be analyzed, distributed approaches like MapReduce are required. So far, the state-of-the-art similarity join approaches based on MapReduce mainly focused on the processing of vector data with less than one hundred dimensions. In this paper, we revisit and investigate the performance of different MapReduce-based approximate k-NN similarity join approaches on Apache Hadoop for large volumes of high-dimensional vector data.
Czech name
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Czech description
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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)

Project
<a href="/en/project/GA15-08916S" target="_blank" >GA15-08916S: Efficient subgraph discovery for petabyte-scale web analysis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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