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Locality sensitive hashing for similarity search using mapreduce on large scale data
Authors:
- Radosław Szmit
Abstract
The paper describes a very popular approach to the problem of similarity search, namely methods based on Locality Sensitive Hashing (LSH). To make coping with large scale data possible, these techniques have been used on the distributed and parallel computing framework for efficient processing using MapReduce paradigm from its open source implementation Apache Hadoop. © 2013 Springer-Verlag Berlin Heidelberg.
- Record ID
- UAM20830d3539b0469bbf7ae9dff6a8ea28
- Author
- Pages
- 171-178
- Book
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Lecture Notes in Computational Vision and Biomechanics, 2013, 171-178 p., ISBN 9783642386336
- ASJC Classification
- ; ; ; ; ; ; ;
- DOI
- DOI:10.1007/978-3-642-38634-3_19 Opening in a new tab
- Language
- (en) English
- Score (nominal)
- 0
- Score
- = 0.0, 19-03-2020, MonographChapterAuthor
- Publication indicators
- = 8; : 2014 = 0.678
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- Uniform Resource Identifier
- https://researchportal.amu.edu.pl/info/article/UAM20830d3539b0469bbf7ae9dff6a8ea28/
- URN
urn:amu-prod:UAM20830d3539b0469bbf7ae9dff6a8ea28
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or PerishOpening in a new tab system.