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Processing of OLAP and SOLAP Queries on Parallel and Distributed Environments

Grant number: 18/22277-8
Support type:Regular Research Grants
Duration: February 01, 2019 - April 30, 2021
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Cristina Dutra de Aguiar Ciferri
Grantee:Cristina Dutra de Aguiar Ciferri
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil


Processing OLAP (on-line analytical processing) queries is very expensive because of the huge volume of data stored in the data warehouse and the fact that they perform star-join. Processing SOLAP queries is even more expensive, since spatial data warehouses are more voluminous and perform the star-join extended with costly topological predicates. The processing of OLAP and SOLAP queries can be benefited by the use of computational environments with large storage and processing capacities. In these environments, it is common the use of parallel and distributed programming paradigms, such as the Apache Hadoop MapReduce and the Apache Spark frameworks. Recently, these frameworks have been extended by DSDMSs (distributed spatial data management systems), which aim to store, index, and process huge volumes of spatial data (e.g.: SpatialHadoop e SpatialSpark). This research project is aimed to propose solutions for the efficient processing of OLAP and SOLAP queries on parallel and distributed environments. Its specific objectives are: (i) the star-join processing considering slice and dice queries with low selectivity; (ii) the star-join processing considering drill-down, roll-up, and drill-across queries with high and low selectivity; (iii) a comparative analyses of DSDMSs available in the literature; and (iv) the star-join processing extended with topological predicates, using as a basis functionalities provided by DSDMSs. (AU)

Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
DE CARVALHO CASTRO, JOAO PEDRO; CHAVES CARNIEL, ANDERSON; DUTRA DE AGUIAR CIFERRI, CRISTINA. Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective. SOFTWARE-PRACTICE & EXPERIENCE, v. 50, n. 12 AUG 2020. Web of Science Citations: 0.
BRITO, JAQUELINE J.; MOSQUEIRO, THIAGO; CIFERRI, RICARDO R.; CIFERRI, CRISTINA D. A. Random access with a distributed Bitmap Join Index for Star Joins. HELIYON, v. 6, n. 2 FEB 2020. Web of Science Citations: 0.
CARNIEL, ANDERSON C.; CIFERRI, RICARDO R.; CIFERRI, CRISTINA D. A. FESTIval: A versatile framework for conducting experimental evaluations of spatial indices. METHODSX, v. 7, 2020. Web of Science Citations: 0.
CARNIEL, ANDERSON C.; CIFERRI, RICARDO R.; CIFERRI, CRISTINA D. A. A generic and efficient framework for flash-aware spatial indexing. INFORMATION SYSTEMS, v. 82, p. 102-120, MAY 2019. Web of Science Citations: 0.

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