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

Hermano Lustosa 1 Anderson da Silva 1 Daniel da Silva 1 Patrick Valduriez 2 Fábio Porto 1
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : 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.
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Submitted on : Wednesday, February 17, 2021 - 3:10:27 PM
Last modification on : Tuesday, February 23, 2021 - 3:25:35 AM
Long-term archiving on: : Tuesday, May 18, 2021 - 7:03:56 PM


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  • HAL Id : lirmm-03144324, version 1



Hermano Lustosa, Anderson da Silva, Daniel da Silva, Patrick Valduriez, Fábio Porto. SAVIME: An Array DBMS for Simulation Analysis and ML Models Predictions. Journal of Information and Data Management, Brazilian Computer Society, 2020, 11 (3), pp.247-264. ⟨lirmm-03144324⟩



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