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| Author(s): |
Hugo Armando Gualdron Colmenares
Total Authors: 1
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| 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: | 2015-11-23 |
| Examining board members: |
José Fernando Rodrigues Junior;
Ricardo José Gabrielli Barreto Campello;
Marcos Gonçalves Quiles;
Marcela Xavier Ribeiro
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| Advisor: | José Fernando Rodrigues Junior |
| Abstract | |
Data analysis techniques can be useful in decision-making processes, when patterns of interest can indicate trends in specific domains. Such trends might support evaluation, definition of alternatives, or prediction of events. Currently, datasets have increased in size and complexity, posing challenges to modern hardware resources. In the case of large datasets that can be represented as graphs, issues of visualization and scalable processing are of current concern. Distributed frameworks are commonly used to deal with this data, but the deployment and the management of computational clusters can be complex, demanding technical and financial resources that can be prohibitive in several scenarios. Therefore, it is desirable to design efficient techniques for processing and visualization of large scale graphs that optimize hardware resources in a single computational node. In this course of action, we developed a visualization technique named StructMatrix to find interesting insights on real-life graphs. In addition, we proposed a graph processing framework M-Flash that used a novel, bimodal block processing strategy (BBP) to boost computation speed by minimizing I/O cost. Our results show that our visualization technique allows an efficient and interactive exploration of big graphs and our framework MFlash significantly outperformed all state-of-the-art approaches based on secondary memory. Our contributions have been validated in peer-review events demonstrating the potential of our finding in fostering the analytical possibilities related to large-graph data domains. (AU) | |
| FAPESP's process: | 13/03906-0 - Visualization and processing of planetary-scale graphs using vertex-centric high performance techniques |
| Grantee: | Hugo Armando Gualdron Colmenares |
| Support Opportunities: | Scholarships in Brazil - Master |