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Erschienen in:
Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Healthcare Decision-Making Over a Geographic, Socioeconomic, and Image Data Warehouse

verfasst von : Guilherme M. Rocha, Piero L. Capelo, Cristina D. A. Ciferri

Erschienen in: ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Recursive Expressions for SPARQL Property Paths
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Metadaten
Titel
Healthcare Decision-Making Over a Geographic, Socioeconomic, and Image Data Warehouse
verfasst von
Guilherme M. Rocha
Piero L. Capelo
Cristina D. A. Ciferri
Copyright-Jahr
2020
Buch
ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

Print ISBN: 978-3-030-55813-0

Electronic ISBN: 978-3-030-55814-7

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-55814-7_7