Binary Sketches for Secondary Filtering

MÍČ, Vladimír. Binary Sketches for Secondary Filtering. In 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval July 25-30, 2020 (Xi'an, China). 2020.

Basic information

• Original name: Binary Sketches for Secondary Filtering
• Authors: MÍČ, Vladimír (203 Czech Republic, guarantor, belonging to the institution).
• Edition: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval July 25-30, 2020 (Xi'an, China), 2020.

Other information

• Original language: English
• Type of outcome: Requested lectures
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• WWW: Conference Program
• RIV identification code: RIV/00216224:14330/20:00116742
• Organization unit: Faculty of Informatics
• Keywords in English: Similarity Search; Metric Space Transformation; Hamming Space; Similarity Filtering
• Tags: International impact

Abstract

Invited talk and discussion (30 minutes, together) at the ACM SIGIR conference (A* rank) about the journal article "Binary Sketches for Secondary Filtering" published in the ACM TOIS journal (2018). The abstract of the article and the presentation: "This paper addresses the problem of matching the most similar data objects to a given query object. We adopt a generic model of similarity that involves the domain of objects and metric distance functions only. We examine the case of a large dataset in a complex data space which makes this problem inherently difficult. Many indexing and searching approaches have been proposed but they have often failed to efficiently prune complex search spaces and access large portions of the dataset when evaluating queries. We propose an approach to enhancing the existing search techniques so as to significantly reduce the number of accessed data objects while preserving the quality of the search results. In particular, we extend each data object with its sketch, a short binary string in Hamming space. These sketches approximate the similarity relationships in the original search space, and we use them to filter out non-relevant objects not pruned by the original search technique. We provide a~probabilistic model to tune the parameters of the sketch-based filtering separately for each query object. Experiments conducted with different similarity search techniques and real-life datasets demonstrate that the secondary filtering can speed-up similarity search several times."

Links

• EF16_019/0000822, research and development project: Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur