Large-Scale Distributed Locality-Sensitive Hashing for General Metric Data

Silva, Eliezer S.; Teixeira, Thiago; Teodoro, George; Valle, Eduardo

doi:10.1007/978-3-319-11988-5_8

Cited by 8 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A busca por similaridade deve ser capaz de trabalhar com bases de dados que crescem rápido conforme o tempo passa, oferencendo tempos de resposta baixos para o usuário final. Essas demandas incentivaram o desenvolvimento de métodos que utilizem técnicas de alto desempenho, bem como implementações escaláveis em ambientes distribuídos [Muja and Lowe 2014, Stupar et al 2010, Bahmani et al 2012, Johnson et al 2017, Moise et al 2013, Andrade et al 2017, Wieschollek et al 2016, Teixeira et al 2013, Silva et al 2014.…”

Section: Soluções Distribuídas Para Indexação E Busca Aproximadaunclassified

Paralelizacão e Otimizações do Algoritmo de Indexação de Dados Multimídia baseado em Quantização

Fernandes

Teodoro

2019

REIC

Self Cite

View full text Add to dashboard Cite

Nesse artigo é apresentada uma paralelização eﬁciente do algoritmo de busca por similaridade Product Quantization Approximate Nearest Neighbor Search (PQANNS). Esse método pode responder consultas com uma demanda reduzida de memória e, juntamente com a paralelização proposta, pode lidar de forma eﬁciente com grandes bases de dados. A execução utilizando 128 nós/3584 núcleos de CPU foi capaz de atingir uma eﬁciência do paralelismo de 0.97 em uma base de dados contendo 256 bilhões de descritores SIFT.

show abstract

Section: Soluções Distribuídas Para Indexação E Busca Aproximadaunclassified

Paralelizacão e Otimizações do Algoritmo de Indexação de Dados Multimídia baseado em Quantização

Fernandes

Teodoro

2019

REIC

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other probabilistic mechanisms have also been proposed, for example [2,17,18]. These are all quite similar in outline but with different ways of defining regional boundaries and treating the proxy space.…”

Section: Search Using Fixed Reference Objectsmentioning

confidence: 99%

Querying Metric Spaces with Bit Operations

Connor

Dearle

2018

Similarity Search and Applications

View full text Add to dashboard Cite

Metric search techniques can be usefully characterised by the time at which distance calculations are performed during a query. Most exact search mechanisms use a "just-in-time" approach where distances are calculated as part of a navigational strategy. An alternative is to use a "one-time" approach, where distances to a fixed set of reference objects are calculated at the start of each query. These distances are typically used to re-cast data and queries into a different space where querying is more efficient, allowing an approximate solution to be obtained. In this paper we use a "one-time" approach for an exact search mechanism. A fixed set of reference objects is used to define a large set of regions within the original space, and each query is assessed with respect to the definition of these regions. Data is then accessed if, and only if, it is useful for the calculation of the query solution. As dimensionality increases, the number of defined regions must increase, but the memory required for the exclusion calculation does not. We show that the technique gives excellent performance over the SISAP benchmark data sets, and most interestingly we show how increases in dimensionality may be countered by relatively modest increases in the number of reference objects used. 1 Context To set a formal context, we are interested in searching a (large) finite set of objects S which is a subset of an infinite set U , where (U, d) is a metric space: that is, an ordered pair (U, d), where U is a domain of objects and d is a total distance function d : U ×U → R, satisfying postulates of non-negativity, identity, symmetry, and triangle inequality [20]. The general requirement is to efficiently find members of S which are similar to an arbitrary member of U given as a query, where the distance function d gives the only way by which any two objects may be compared. There are many important practical examples captured by this mathematical framework, see for example [16, 20]. The simplest type of similarity query is the range search query: for some threshold t, based on a query q ∈ U , the solution set is R = {s ∈ S| d(q, s) ≤ t}. The essence of metric search is to spend time pre-processing the finite set S so that solutions to queries can be efficiently calculated using only distances among objects. In all cases therefore, distances between the data and selected This is a post-peer-review, pre-copyedit version of a paper published in Marchand-Maillet S., Silva Y., Chávez E.

show abstract

Evaluation of Static/Dynamic Cache for Similarity Search Engines

Solar

Gil-Costa

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Large-Scale Distributed Locality-Sensitive Hashing for General Metric Data

Cited by 8 publications

References 14 publications

Paralelizacão e Otimizações do Algoritmo de Indexação de Dados Multimídia baseado em Quantização

Paralelizacão e Otimizações do Algoritmo de Indexação de Dados Multimídia baseado em Quantização

Querying Metric Spaces with Bit Operations

Evaluation of Static/Dynamic Cache for Similarity Search Engines

Contact Info

Product

Resources

About