Abstract
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.
Supported by the São Paulo Research Foundation (FAPESP), the Brazilian Federal Research Agency CNPq, and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (CAPES), Finance Code 001. G.M.R. and C.D.A.C. acknowledge support from FAPESP grants #2018/10607-3 and #2018/22277-8, respectively.
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Rocha, G.M., Capelo, P.L., Ciferri, C.D.A. (2020). Healthcare Decision-Making Over a Geographic, Socioeconomic, and Image Data Warehouse. In: Bellatreche, L., et al. ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium. TPDL ADBIS 2020 2020. Communications in Computer and Information Science, vol 1260. Springer, Cham. https://doi.org/10.1007/978-3-030-55814-7_7
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