Managing Sparse Spatio-Temporal Data in SAVIME: an Evaluation of the Ph-tree Index

• Stiw Herrera Laboratório Nacional de Computação Científica (LNCC)
• Larissa Miguez da Silva Laboratório Nacional de Computação Científica (LNCC)
• Paulo Ricardo Reis Laboratório Nacional de Computação Científica (LNCC)
• Anderson Silva Laboratório Nacional de Computação Científica (LNCC) / DEXL Lab
• Fabio Porto Laboratório Nacional de Computação Científica (LNCC) / DEXL Lab

DOI: https://doi.org/10.5753/sbbd.2021.17895

Resumo

Scientific data is mainly multidimensional in its nature, presenting interesting opportunities for optimizations when managed by array databases. However, in scenarios where data is sparse, an efficient implementation is still required. In this paper, we investigate the adoption of the Ph-tree as an in-memory indexing structure for sparse data. We compare the performance in data ingestion and in both range and punctual queries, using SAVIME as the multidimensional array DBMS. Our experiments, using a real weather dataset, highlights the challenges involving providing a fast data ingestion, as proposed by SAVIME, and at the same time efficiently answering multidimensional queries on sparse data.

Palavras-chave: Array Databases, Indexing, Ph-tree

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