Data and task orchestration defined by spatio-temporal variables for healthcare data science services

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dc.contributor.affiliationCinvestav Tamaulipas Cd.Victoria, Tamaulipas, Mexico
dc.contributor.emailjose.morin@cinvestav.mx
dc.creatorMorin-Garcia, Jose Carloses_ES
dc.creatorBarron-Lugo, J. Armandoes_ES
dc.creatorGonzalez-Compean, J. L.es_ES
dc.creatorLopez-Arevalo, Ivanes_ES
dc.creatorCarretero, Jesuses_ES
dc.creatorCordero-Oropeza, Marthaes_ES
dc.date2022
dc.date.accessioned2025-06-04T17:49:20Z
dc.date.accessioned2026-03-27T15:32:19Z
dc.date.available2025-06-04T17:49:20Z
dc.date.issued2022
dc.date.published2022
dc.descriptionData science services have become a solution for healthcare or ganizations to take advantage of the large volumes of data (e.g., data lakes and data warehouses) produced during the interaction of healthcare staff with patients and government agencies. However, the data orchestration for these services is not trivial when deal ing with multiple data sources where decision-making processes should combine them to create a single solid information piece (big picture) for making inferences or predictions. In this paper, we present a data and task orchestration method for supporting healthcare data science services. This method considers stages such as data fusion/integration for enabling the crossing of information, computing splits for producing, on-the-fly and on-demand, data subsets by using spatio-temporal variables, converting splited data into information, consolidation of information into segments to create a big picture of data and, in the last stage, makes available data segments for consumption on decision-making processes by using spatio-temporal queries. A case study based on the fusion of healthcare data sources about psychiatric, drug consumption, and macro-economics was conducted by using a prototype of the data orchestration proposed in this paper. The evaluation revealed the flexibility of this data orchestration approach to convert multiple data sources into useful information for healthcare decision-making processes.es_ES
dc.formatPDFes_ES
dc.identifierJC69DIEP23es_ES
dc.identifier.doi10.1145/3569192.3569208
dc.identifier.organizacionInstituto Nacional de Psiquiatría Ramón de la Fuente Muñiz
dc.identifier.placeEstados Unidos
dc.identifier.urihttps://doi.org/10.1145/3569192.3569208
dc.identifier.urihttps://repositorio.inprf.gob.mx/handle/123456789/8379
dc.language.isoenges_ES
dc.publisherACMes_ES
dc.relation95-101
dc.rightsAcceso Cerradoes_ES
dc.sourceICBRA 2022, September 18–20, 2022
dc.subject.kwData orchestration
dc.subject.kwData fusion
dc.subject.kwMedical data analytics
dc.subject.kwSpatio-temporal studies
dc.titleData and task orchestration defined by spatio-temporal variables for healthcare data science serviceses_ES
dc.typeArtículoes_ES

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