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A Method for Collecting Provenance Data: A Case Study in a Brazilian Hemotherapy Center

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Data and Information in Online Environments (DIONE 2020)

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Abstract

Data provenance is a process that aims to provide an overview of the origin of data used by information systems. It focuses on the origin of the data, especially on identifying the data sources and the transformations the data has undergone over time. This paper proposes a method for data collection based on the Provenance Model (PROV-DM), to be applied on Brazilian hemotherapy centers. Storing data on anemia indices using data provenance is the overall purpose of it. This work uses concepts of data provenance, knowledge provenance and scientific workflow techniques. It is an exploratory research, of practical and deductive nature, with application of a case study. Actual data was extracted from reports generated by a Brazilian hemotherapy center, provided from 2000 to 2018. People unsuitable for blood donation, who had favorable anemia rates to be rejected, were quantified and analyzed. A total of 197,551 blood donor candidates who attended the hemotherapy center in 19 years were analyzed. In the end, it was possible to quantify the unfit candidates with the highest index of anemia. A total of 1,011 male and 4,039 female candidates were accounted for, totaling 4.02% and 16.09% respectively of donors unfit for blood donations.

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Authors and Affiliations

  1. Federal University of Santa Catarina, Florianópolis, Brazil

    Márcio José Sembay, Douglas Dyllon Jeronimo de Macedo & Moisés Lima Dutra

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  1. Márcio José Sembay
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  2. Douglas Dyllon Jeronimo de Macedo
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  3. Moisés Lima Dutra
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Correspondence to Márcio José Sembay .

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  1. Universidade de São Paulo, São Paulo, Brazil

    Rogério Mugnaini

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Sembay, M.J., de Macedo, D.D.J., Lima Dutra, M. (2020). A Method for Collecting Provenance Data: A Case Study in a Brazilian Hemotherapy Center. In: Mugnaini, R. (eds) Data and Information in Online Environments. DIONE 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-030-50072-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-50072-6_8

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  • Online ISBN: 978-3-030-50072-6

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