Monitoring ArchiMate Models for DataBio Project

Chaabouni, Kaïs; Bagnato, Alessandra; García-Domínguez, Antonio

doi:10.1007/978-3-030-35333-9_42

Cited by 2 publications

(2 citation statements)

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“…For each metric, we define a query for Hawk to interrogate from the models. After this, we store and visualize the collected measurement via the Measure Platform [12].…”

Section: Models Quality Metricsmentioning

confidence: 99%

“…In order to monitor the quality of the models, we have defined a metric that evaluates the efficiency of the modelling process, the comprehensibility of the models and the model size. The metric discussed here could be applied also in other projects, where the modelling tool Modelio or the modelling language ArchiMate are in use [12]. The proposed metric indicates that the quality level in DataBio is acceptable as comes to the efficiency of the modelling process and the comprehensibility of the models.…”

Section: Conclusion and Future Visionmentioning

confidence: 99%

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Data Pipelines: Modeling and Evaluation of Models

Chaabouni¹,

Bagnato²

2021

Big Data in Bioeconomy

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This chapter outlines the utility of data pipelines modeling in the context of a data driven project and enumerates metrics for evaluating the quality of the data modeling regarding the readability and the comprehensibility of the models. We start with explaining the challenges surrounding the DataBio project that led to the adoption of data pipelines modeling using the Enterprise Architecture language ArchiMate. Then we present the data modeling process with examples from DataBio pilot studies starting with modeling software components provided by project stakeholders and ending up with integration of components into data pipelines that achieve the data analytics lifecycle intended by the pilot study. We end the chapter with the evaluation of the quality of DataBio data pipelines models with metrics collected by a monitoring tool for ArchiMate models.

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“…For each metric, we define a query for Hawk to interrogate from the models. After this, we store and visualize the collected measurement via the Measure Platform [12].…”

Section: Models Quality Metricsmentioning

confidence: 99%

Section: Conclusion and Future Visionmentioning

confidence: 99%