Autor(es)

Rocha, Guilherme M. ; Capelo, Piero L. ; Ciferri, Cristina D. A.

• Tipo de documento: Artigo Científico
• Fonte: ADBIS, TPDL AND EDA 2020 COMMON WORKSHOPS AND DOCTORAL CONSORTIUM; v. 1260, p. 13-pg., 2020-01-01.

Resumo

Geographic, socioeconomic, and image data enrich the range of analysis that can be achieved in the healthcare decision-making. In this paper, we focus on these complex data with the support of a data warehouse. We propose three designs of star schema to store them: jointed, split, and normalized. We consider healthcare applications that require data sharing and manage huge volumes of data, where the use of frameworks like Spark is needed. To this end, we propose SimSparkOLAP, a Spark strategy to efficiently process analytical queries extended with geographic, socioeconomic, and image similarity predicates. Performance tests showed that the normalized schema provided the best performance results, followed closely by the jointed schema, which in turn outperformed the split schema. We also carried out examples of semantic queries and discuss their importance to the healthcare decision-making. (AU)

• Processo FAPESP: 18/10607-3 - Processamento de consultas analíticas em ambientes computacionais paralelos e distribuídos
• Beneficiário: Guilherme Muzzi da Rocha
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 18/22277-8 - Processamento de Consultas OLAP e SOLAP em Ambientes Computacionais Paralelos e Distribuídos
• Beneficiário: Cristina Dutra de Aguiar
• Modalidade de apoio: Auxílio à Pesquisa - Regular