NOVÁK, David. Multi-modal Similarity Retrieval with a Shared Distributed Data Store. In Jung, Jason J, Badica, Costin, Kiss, Attila. Scalable Information Systems: 5th International Conference, INFOSCALE 2014, Seoul, South Korea, September 25-26, 2014, Revised Selected Papers. New York: Springer International Publishing, 2015, p. 28-37. ISBN 978-3-319-16867-8. Available from: https://dx.doi.org/10.1007/978-3-319-16868-5_3.
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Basic information
Original name Multi-modal Similarity Retrieval with a Shared Distributed Data Store
Authors NOVÁK, David (203 Czech Republic, guarantor, belonging to the institution).
Edition New York, Scalable Information Systems: 5th International Conference, INFOSCALE 2014, Seoul, South Korea, September 25-26, 2014, Revised Selected Papers, p. 28-37, 10 pp. 2015.
Publisher Springer International Publishing
Other information
Original language English
Type of outcome Proceedings paper
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
Publication form printed version "print"
RIV identification code RIV/00216224:14330/15:00081206
Organization unit Faculty of Informatics
ISBN 978-3-319-16867-8
ISSN 1867-8211
Doi http://dx.doi.org/10.1007/978-3-319-16868-5_3
Keywords in English similarity search; multi-modal search; Big Data; scalability
Tags DISA
Tags International impact, Reviewed
Changed by Changed by: RNDr. David Novák, Ph.D., učo 4335. Changed: 18/11/2015 21:16.
Abstract
We propose a generic system architecture for large-scale similarity search in various types of digital data. The architecture combines contemporary highly-scalable distributed data stores with recent efficient similarity indexes and also with other types of search indexes. The system is designed to provide several types of queries – distance-based similarity queries, term-based queries, attribute queries, and advanced queries combining several search aspects (modalities). The first part of this work is devoted to the generic architecture and to description of a similarity index PPP-Codes that is suitable for our system. In the second part, we describe a specific instance of this architecture that manages a 106 million image collection providing content-based visual search, keyword search, attribute-based access, and their combinations.
Links
GBP103/12/G084, research and development projectName: Centrum pro multi-modální interpretaci dat velkého rozsahu
Investor: Czech Science Foundation
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