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Scalable Similarity Search

Similarity search is the task of identifying, in a collection of items, the ones that are similar to a given query item. This task has a range of important applications (e.g. in information retrieval, pattern recognition, statisti... ver más

30/04/2019

IT UNIVERSITY OF C...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

ITUNIVERSITETET I KOBENHAVN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2019-04-30 No tenemos la información de la convocatoria

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1 Participantes

IT UNIVERSITY OF COPENHAGEN

0.00€ | Lider

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Líder del proyecto

ITUNIVERSITETET I KOBENHAVN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Similarity search is the task of identifying, in a collection of items, the ones that are similar to a given query item. This task has a range of important applications (e.g. in information retrieval, pattern recognition, statistics, and machine learning) where data sets are often big, high dimensional, and possibly noisy. State-of-the-art methods for similarity search offer only weak guarantees when faced with big data. Either the space overhead is excessive (1000s of times larger than the space for the data itself), or the work needed to report the similar items may be comparable to the work needed to go through all items (even if just a tiny fraction of the items are similar). As a result, many applications have to resort to the use of ad-hoc solutions with only weak theoretical guarantees. This proposal aims at strengthening the theoretical foundation of scalable similarity search, and developing novel practical similarity search methods backed by theory. In particular we will: - Leverage new types of embeddings that are kernelized, asymmetric, and complex-valued. - Consider statistical models of noise in data, and design similarity search data structures whose performance guarantees are phrased in statistical terms. - Build a new theory of the communication complexity of distributed, dynamic similarity search, emphasizing the communication bottleneck present in modern computing infrastructures. The objective is to produce new methods for similarity search that are: 1) Provably robust, 2) scalable to large and high-dimensional data sets, 3) substantially more resource efficient than current state-ofthe- art solutions, and 4) able to provide statistical guarantees on query answers. The study of similarity search has been an incubator for techniques (e.g. locality-sensitive hashing and random projections) that have wide-ranging applications. The new techniques developed in this project are likely to have significant impacts beyond similarity search.

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