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Boosting the applicability of metric access methods

Grant number: 15/24602-5
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): April 01, 2016
Effective date (End): March 31, 2017
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal researcher:Agma Juci Machado Traina
Grantee:Jessica Andressa de Souza
Supervisor abroad: Sebastian Michel
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: University of Kaiserslautern, Germany  
Associated to the scholarship:13/21378-1 - Study and Development of Metric Access Methods Using Semantic Data Grouping, BP.DR

Abstract

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 the processing time of similarity queries. Furthermore, MAMs have been applied to support data mining techniques, for example, to speed up the data clustering detection process. However, the results presented to the users are not always the desired ones, because often the user's perception considers the query responses in a given context (i.e., the results presented are different from the expected ones). Thus, the objective of this internship project is to develop research on methods for efficient and effective similarity search in large and heterogeneous data collections. The internship project will be hosted at the Kaiserslautern University of Technology and supervised by Prof. Sebastian Michel. (AU)