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

Authors

  • Patrick Valduriez Inria, University of Montpellier, CNRS, LIRMM, France
  • Fábio André Machado Porto Laboratório Nacional de Computação Científica
  • Daniel Nascimento Ramos da Silva Laboratório Nacional de Computação Laboratório Nacional de Computação CientíficaCientífica
  • Hermano Lourenço Souza Lustosa Laboratório Nacional de Computação Científica
  • Anderson Chaves da Silva Laboratório Nacional de Computação Científica

DOI:

https://doi.org/10.5753/jidm.2020.1872

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 system 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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Published

2020-12-30

Issue

Section

SBBD 2019