Algorithmic exploration of axiom spaces for efficient similarity search at large scale
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10131930" target="_blank" >RIV/00216208:11320/12:10131930 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007/978-3-642-32153-5_4" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-642-32153-5_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-32153-5_4" target="_blank" >10.1007/978-3-642-32153-5_4</a>
Alternative languages
Result language
angličtina
Original language name
Algorithmic exploration of axiom spaces for efficient similarity search at large scale
Original language description
Similarity search is becoming popular in even more disciplines, such as multimedia databases, bioinformatics, social networks, to name a few. The existing indexing techniques often assume the metric space model that could be too restrictive from the domain point of view. Hence, many modern applications that involve complex similarities do not use any indexing and use just sequential search, so they are applicable only to small databases. In this paper we revisit the assumptions which persist in the mainstream research of content-based retrieval. Leaving the traditional indexing paradigms such as the metric space model, our goal is to propose alternative methods for indexing that shall lead to high-performance similarity search. We introduce the designof the algorithmic framework SIMDEX for exploration of analytical properties (axioms) useful for indexing that hold in a given complex similarity space but were not discovered so far. Consequently, the known axioms will be localized as a
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/GAP202%2F11%2F0968" target="_blank" >GAP202/11/0968: Large-scale Nonmetric Similarity Search in Complex Domains</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Lecture Notes in Computer Science
ISSN
0302-9743
e-ISSN
—
Volume of the periodical
7404
Issue of the periodical within the volume
2012
Country of publishing house
DE - GERMANY
Number of pages
14
Pages from-to
40-53
UT code for WoS article
—
EID of the result in the Scopus database
—