Open AccessManaging Sparse Spatio-Temporal Data in SAVIME: an Evaluation of the Ph-tree Index
Author(s) -
Stiw Herrera,
Larissa Miguez da Silva,
Paulo Ricardo da Costa Reis,
Anderson Silva,
Fábio Porto
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbbd.2021.17895
Subject(s) - computer science , search engine indexing , sparse matrix , data mining , tree (set theory) , range (aeronautics) , multidimensional data , data structure , index (typography) , data modeling , information retrieval , database , mathematics , world wide web , mathematical analysis , physics , materials science , quantum mechanics , composite material , gaussian , programming language
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.