CV Lattes
Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
I am a Ph.D. in Computer Science at the University of São Paulo (USP) in Brazil. Part of my Ph.D. project included a visiting period of one year at the Department of Computer Science of the Technical University of Kaiserslautern (TU) in Germany. Master in Computer Science at Federal University of ABC (UFABC) and Bachelor in Information Systems at Federal University of Grande Dourados (UFGD). During my studies, I focused my research on clustering detection algorithms for processing large datasets on metric indexing structures. Areas of interest include: 1. Data Mining and Knowledge Discovery: Researching issues related to indexing structures and cluster detection. 2. Similarity Queries: Researching issues related to similarity queries on large complex datasets, e.g. image dataset. 3. Software development/Engineering (Source: Lattes Curriculum)
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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…
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…
In the last years, due to advances in technology, both the amount and variety of data available for analysis have significantly increased. This scenario has intensified the development of strategies, for fast and automatic processing of such data. Metric access methods (MAMs) are strategic for these purposes, because they aim at indexing the data, bringing effective ways for accelerating …
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