Study and Development of Metric Access Methods Using Semantic Data Grouping

• Grant number: 13/21378-1
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: March 01, 2014
• End date: July 31, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Agma Juci Machado Traina
• Grantee: Jessica Andressa de Souza
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 15/24602-5 - Boosting the applicability of metric access methods, BE.EP.DR

Abstract

The significant growth in both the amount and variety of data available for analysis has increased the need to develop methodologies for understanding, processing and summarizing data in a quick and automatic manner. One of the features that has proven critical for understanding and learning from large amounts of data is related to the data organization into groups by synthesis of what is common. Clustering detection is among the techniques used in the data mining field to aid data analysis by synthesis. This technique aims to group the data into groups of similar elements based on measured or perceived data features. The development of such a technique has been extensively explored in the literature. However, clustering algorithms that employ optimization strategies not always correspond to the most appropriate partitioning. This is due to the strategy adopted by these algorithms is based on a process unsupervised. Thus, the proposed work presented in this project aims to contribute to the treatment of this issue, approaching the study and the development of strategies that result in better quality clusters semantic. Therefore, should be exploited to develop new policies for building metric access methods, for the optimization of algorithms for detecting data clusters, and the incorporation of these algorithms in Management System Database (DBMS).

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

NESSO-, MARCOS R., JR.; CAZZOLATO, MIRELA T.; SCABORA, LUCAS C.; OLIVEIRA, PAULO H.; SPADON, GABRIEL; DE SOUZA, JESSICA A.; OLIVEIRA, WILLIAN D.; CHINO, DANIEL Y. T.; RODRIGUES-, JOSE F., JR.; TRAINA, AGMA J. M.; et al. RAFIKI: Retrieval-Based Application for Imaging and Knowledge Investigation. 2018 31ST IEEE INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS 2018), v. N/A, p. 6-pg., 2018-01-01. (13/21378-1, 17/08376-0, 16/17330-1, 15/15392-7, 16/17078-0)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

SOUZA, Jessica Andressa de. Clustering complex data for processing constrained similarity queries. 2018. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.