Mining frequent patterns in time series to support decision-making in agrometeorology

Author(s)

Daniel Yoshinobu Takada Chino

• 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: 2014-03-18

Examining board members

Agma Juci Machado Traina; Ana Maria Heuminski de Avila; Gustavo Enrique de Almeida Prado Alves Batista

• Advisor: Agma Juci Machado Traina

Abstract

Dealing with large volumes of complex data is a challenging task that has motivated many researchers around the world. Time series is a type of complex data that is growing in importance due to the increasing demand of sensors for surveillance and monitoring. Thus, mining information from large volumes of time series to support decision making is a valuable activity nowadays. This Master dissertation goes in this direction, as it proposes new algorithms and methods to mine and index time series. The novelty of the TrieMotif, a new algorithm to mine frequent patterns (motifs) from time series employing a trie structure that allows clever comparison between the sequences, as well as the Telesto index structure based on suffix trees area presented and discussed in the context of agrometeorological and climatological data, being the two main contributions of this work. The dissertation shows that the proposed algorithms are scalable, being suitable to big data, and when compared to the competitors they always presented the best results (AU)

• FAPESP's process: 11/15017-0 - Integrated Mining of Multi-Modal Data for Decision Making in Agrometeorology
• Grantee: Daniel Yoshinobu Takada Chino
• Support Opportunities: Scholarships in Brazil - Master