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Including distance functions and features extractors to support similarity queries

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Marcos Vinícius Naves Bêdo
Total Authors: 1
Document type: Master's Dissertation
Press: São Carlos.
Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
Defense date:
Examining board members:
Caetano Traina Junior; Sandra Aparecida de Amo; Paulo Mazzoncini de Azevedo Marques
Advisor: Caetano Traina Junior

Database Management Systems (DBMS) can deal with large amount of data. The queries on those systems obey the total order relation (TOR), domain where simple data such as numbers or strings are defined. In the case of complex data (e.g.: medical images, audio or temporal time-series) which does not obey the TOR properties, it\'s mandatory a new approach that can retrieve complex data by content with time skilful and proper semantics. To do so, the literature presents us, as consolidated paradigm, the similarity queries. This paradigm is the base of many computer aided applications (e.g.: Content-Based Medical Image Retrieval (CBMIR) and Content-Based Audio Retrieval (CBAR)) and include several research areas such as features extraction, distance functions and metrical access methods (MAM). Developing new features extractors methods and new distance functions (and combine them) are crucial to reduce the semantic gap between the content-based applications and the users. The MAM are responsible to provide fast and scalable answer to the systems. Integrate all those functionalities in one framework that can provide support to similarity queries inside a DBMS remains a huge challenge. The main objective of this work is extend the initial resources of the system SIREN, inserting new features extractor methods and distance functions to medical images, audio and financial time-series, turning it into a framework. All components may be used by extended Structured Query Language (SQL) commands. The SQL can be directly used by computer-aided applications (AU)

FAPESP's process: 11/05301-3 - Integrating distance functions and feature extractors into DBMS to perform similarity queries
Grantee:Marcos Vinicius Naves Bêdo
Support type: Scholarships in Brazil - Master