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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Querying on large and complex databases by content: Challenges on variety and veracity regarding real applications

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Author(s):
Traina, Agma J. M. [1] ; Brinis, Safia [1] ; Pedrosa, V, Glauco ; Avaihais, Letricia P. S. [1] ; Traina Jr, Caetano
Total Authors: 5
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, Comp Sci Dept, Sao Carlos, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: INFORMATION SYSTEMS; v. 86, p. 10-27, DEC 2019.
Web of Science Citations: 0
Abstract

The amount and variety of digital data currently being generated, stored and analyzed, including images, videos, and time series, have brought challenges to data administrators, analysts and developers, who struggle to comply with the expectations of both data owners and end users. The majority of the applications demand searching complex data by taking advantage of queries that analyze different aspects of the data, and need the answers in a timely manner. Content-based similarity retrieval techniques are well-suited to handle large databases, because they enable performing queries and analyses using features automatically extracted from the data, without users' intervention. In this paper, we review and discuss the challenges posed to the database and related communities in order to provide techniques and tools that can meet the variety and veracity characteristics of big and complex data, while also considering the aspects of semantical preservation and completeness of the data. Examples and results obtained over a two-decade-long experience with real applications are presented and discussed. (C) 2019 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
Grantee:Agma Juci Machado Traina
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 13/27101-1 - An approach to semantic segmentation exploiting multimodality and temporal characteristics of digital videos
Grantee:Letrícia Pereira Soares Avalhais
Support Opportunities: Scholarships in Brazil - Doctorate