 Real-time data warehouse loading methodology and architecture: a healthcare use caseHanen Bouali, Jalel Akaichi and Ala Gaaloul International Journal of Data Analysis Techniques and Strategies, 2019, vol. 11, issue 4, 310-327 Abstract: In the healthcare context, existing systems suffer from the lack of supporting heterogeneity and dynamism. Consequently, resulting from sensors, streaming data brought another dimension to data mining research. This is due to the fact that, in data streams, only a time window is available. Contrary to the traditional data sources, data streams present new characteristics as being continuous, high-volume, open-ended and concept drift. To analyse event streams, data warehouse seems to be the answer to this problematic. However, classical data warehouse does not incorporate the specificity of event streams that are spatial, temporal, semantic and real-time. For these reasons, we focus inhere on presenting the conceptual modelling, the architecture and loading methodology of the real-time data warehouse by defining a new dimensionality and stereotype for classical data warehouse. To prove the efficiency of our real-time data warehouse, we adapt the model to a medical unit pregnancy care case study which show promising results. Keywords: data warehouse; data analysis; real-time; healthcare. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:11:y:2019:i:4:p:310-327 Access Statistics for this article More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd |
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