| Grant number: | 18/10607-3 |
| Support Opportunities: | Scholarships in Brazil - Master |
| Start date: | April 01, 2019 |
| End date: | June 30, 2020 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Cristina Dutra de Aguiar |
| Grantee: | Guilherme Muzzi da Rocha |
| Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
Abstract Processing OLAP (on-line analytical processing) queries is very expensive because of the huge volume of data stored in the data warehouse and the fact that they perform star-join. This processing can be benefited by the use of computational environments with large storage and processing capacities, such as clusters of computers and cloud computing. In these environments, it is common the use of parallel and distributed programming paradigms, such as the Spark framework. In the literature, there are several approaches that investigate the processing of joins in parallel and distributed environments, including the star-join. However, to the best of our knowledge, none of these approaches investigates the performance of star-joins considering drill-down, roll-up, and drill-across OLAP queries. These queries are of great importance because they are often required in decision-making. In addition, they have specific characteristics, which imply in specific optimizations. This master's project aims to fill this gap in the literature by proposing solutions for the efficient processing of drill-down, roll-up and drill-across OLAP queries, considering the framework Spark. (AU) | |
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