SAVIME: An Array DBMS for Simulation Analysis and ML Models Predictions

Hermano Lourenço Souza Lustosa (1) , Anderson Chaves da Silva (1) , Daniel Nascimento Ramos da Silva (1) , Patrick Valduriez (2) , Fábio André Machado Porto (1)

1 LNCC / MCT - Laboratorio Nacional de Computação Cientifica [Rio de Janeiro]
2 ZENITH - Scientific Data Management

Limitations in current DBMSs prevent their wide adoption in scientific applications. In order to make them benefit from DBMS support, enabling declarative data analysis and visualization over scientific data, we present an in-memory array DBMS called SAVIME. In this work we describe the system SAVIME, along with its data model. Our preliminary evaluation show how SAVIME, by using a simple storage definition language (SDL) can outperform the state-of-the-art array database system, SciDB, during the process of data ingestion. We also show that it is possible to use SAVIME as a storage alternative for a numerical solver without affecting its scalability, making it useful for modern ML based applications.

Mots clés: Scientific Data Management; Multidimensional Array; Machine Learning

Domaines: Base de données [cs.DB]

Hermano Lourenço Souza Lustosa, Anderson Chaves da Silva, Daniel Nascimento Ramos da Silva, Patrick Valduriez, Fábio André Machado Porto. SAVIME: An Array DBMS for Simulation Analysis and ML Models Predictions. Journal of Information and Data Management, 2021, 11 (3), pp.247-264. ⟨10.5753/jidm.2020.2021⟩. ⟨lirmm-03144324⟩