Database Management and Administration
The growing volume of data requires organizations to develop strategies for extracting valuable insights and gaining competitive advantage. This process involves the collection, storage, integration, and analysis of structured, semi-structured, and unstructured data. The research investigates methodologies for managing and transforming these data into useful knowledge to support decision-making.
The focus lies on data-centric artificial intelligence (Data-Centric AI) for data preparation and on large-scale processing techniques. One of the challenges addressed is the parallel and distributed processing of massive volumes of heterogeneous data, common in fields such as bioinformatics, astronomy, and engineering. Scientific workflows are essential for these experiments and are frequently executed on clusters, supercomputers, and cloud environments.
The project also explores frameworks such as Apache Spark, optimizing workflows for large-scale data analysis and management. In addition, it investigates conceptual modeling techniques, ontologies, preprocessing, indexing, and querying in Big Data systems. The research considers approaches based on distributed storage (HDFS), NoSQL databases, NewSQL systems, and object-relational databases, aiming to enhance the efficiency of data handling and analysis.
Faculty Members Involved:
- Rafaelli de Carvalho Coutinho (coordinator)
- Eduardo Soares Ogasawara
- Diego Moreira de Araújo Carvalho
- Jorge de Abreu Soares
- Kele Teixeira Belloze