• français
    • English
  • English 
    • français
    • English
  • Login
JavaScript is disabled for your browser. Some features of this site may not work without it.
BIRD Home

Browse

This CollectionBy Issue DateAuthorsTitlesSubjectsJournals BIRDResearch centres & CollectionsBy Issue DateAuthorsTitlesSubjectsJournals

My Account

Login

Statistics

View Usage Statistics

SimSearch: similarity search framework based on indexing techniques in metric spaces

Thumbnail
Date
2015
Dewey
Organisation des données
Sujet
Indexing Mechanisms; Similarly Search; Query Strategies
DOI
http://dx.doi.org/10.1145/2857218.2857233
Book title
MEDES '15 Proceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems
Author
Richard Chbeir, Yannis Manolopoulos, Victor Pellegrini Mammana, Eduardo Antonio Modena, Agma J. M. Traina, Oscar Salviano Silva Filho, Youakim Badr, Frédéric Andrès
Publisher
ACM Press
Publisher city
New York
Year
2015
ISBN
978-1-4503-3480-8
URI
https://basepub.dauphine.fr/handle/123456789/16138
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Zaragoza, David
Cardinale, Yudith
Rukoz, Marta
Type
Communication / Conférence
Item number of pages
90-97
Abstract (EN)
Similarity search in metric spaces refers to searching elements in data repositories that are similar to an element supplied by the user (query example). Similarity functions are used to determine which elements in the data repositories are similar to the query example and indexing mechanisms are used to improve the efficiency in the search. Classic indexation mechanisms such as LSH, M-Index, and M-Tree behave different according to the dimensionality in the metric space, volume of data repositories, and query strategies. In this paper, we describe SimSearch, a modular and flexible framework for similarity search in metric spaces, which allows to use, analyse, compare, and add several indexation mechanisms, search approaches, and query strategies. SimSearch allows doing queries given one or more example elements to obtain the set of elements more similar to the query examples, using query composition and Skyline. We show the variability of performance of several indexation mechanisms, including LSH-ML (our proposed variant of LSH), with experimental study in the domain of images represented by a feature vector in a high dimensionality metric space and Web Services represented by a vector with the values of Quality of Service (QoS) parameters.

  • Accueil Bibliothèque
  • Site de l'Université Paris-Dauphine
  • Contact
SCD Paris Dauphine - Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16

 Content on this site is licensed under a Creative Commons 2.0 France (CC BY-NC-ND 2.0) license.