Designing Sketches for Similarity Filtering
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F16%3A00088645" target="_blank" >RIV/00216224:14330/16:00088645 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICDMW.2016.0098" target="_blank" >http://dx.doi.org/10.1109/ICDMW.2016.0098</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICDMW.2016.0098" target="_blank" >10.1109/ICDMW.2016.0098</a>
Alternative languages
Result language
angličtina
Original language name
Designing Sketches for Similarity Filtering
Original language description
Abstract: The amounts of currently produced data emphasize the importance of techniques for efficient data processing. Searching big data collections according to similarity of data well corresponds to human perception. This paper is focused on similarity search using the concept of sketches – a compact bit string representations of data objects compared by Hamming distance, which can be used for filtering big datasets. The object-to-sketch transformation is a form of the dimensionality reduction and thus there are two basic contradictory requirements: (1) The length of the sketches should be small for efficient manipulation, but (2) longer sketches retain more information about the data objects. First, we study various sketching methods for data modeled by metric space and we analyse their quality. Specifically, we study importance of several sketch properties for similarity search and we propose a high quality sketching technique.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)
ISBN
9781509054725
ISSN
2375-9232
e-ISSN
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Number of pages
8
Pages from-to
655-662
Publisher name
IEEE
Place of publication
USA
Event location
Barcelona, Španělsko
Event date
Jan 1, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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