Spatio-temporal analysis in multidimensional data streams

Author(s)

Santiago Augusto Nunes

• 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: 2015-04-06

Examining board members

Elaine Parros Machado de Sousa; Gustavo Enrique de Almeida Prado Alves Batista; Gisele Lobo Pappa

• Advisor: Elaine Parros Machado de Sousa

Abstract

Data streams are usually characterized by large amounts of data generated continuously in synchronous or asynchronous potentially infinite processes, in applications such as: meteorological systems, industrial processes, vehicle traffic, financial transactions, sensor networks, among others. In addition, the behavior of the data tends to change significantly over time, defining evolutionary data streams. These changes may mean temporary events (such as anomalies or extreme events) or relevant changes in the process of generating the stream (that result in changes in the distribution of the data). Furthermore, these data sets can have spatial characteristics such as geographic location of sensors, which can be useful in the analysis process. The detection of these behavioral changes considering aspects of evolution, as well as the spatial characteristics of the data, is relevant for some types of applications, such as monitoring of extreme weather events in Agrometeorology researches. In this context, this project proposes a technique to help spatio-temporal analysis in multidimensional data streams containing spatial and non-spatial information. The adopted approach is based on concepts of the Fractal Theory, used for temporal behavior analysis, as well as techniques for data streams handling also hierarchical data structures, allowing analysis tasks that take into account the spatial and non-spatial aspects simultaneously. The developed technique has been applied to agro-meteorological data to identify different behaviors considering different sub-regions defined by the spatial characteristics of the data. Therefore, results from this work include contribution to data mining area and support research in Agrometeorology. (AU)

• FAPESP's process: 11/15829-5 - Detection of anomalies and extreme events in multidimensional data streams.
• Grantee: Santiago Augusto Nunes
• Support Opportunities: Scholarships in Brazil - Master