This chapter intends to propose a solution to the progressive paradigmatic drift that emerges when data is part of a work ow where processes and contexts are not accessible. In this case end users can have di culties in identifying possible anomalies, or events that might be very important for the dataset and its interpretation. Since sustainable development is based on cross-disciplinary studies, this can revert in misunderstanding and di culties to work collaboratively. To address this issue it is proposed to ll the gap between the data and its acquisition through a logging system named EARS that records underway data such as for example: meteo or swell, and events such as: anomalies or acquisition milestones. Once all these information are stored, they can be linked to the data through an OGC compliant metadata model and gathered as summaries as required by several data management initiatives.

Repositioning Data Management Near Data Acquisition

Diviacco P;
2016

Abstract

This chapter intends to propose a solution to the progressive paradigmatic drift that emerges when data is part of a work ow where processes and contexts are not accessible. In this case end users can have di culties in identifying possible anomalies, or events that might be very important for the dataset and its interpretation. Since sustainable development is based on cross-disciplinary studies, this can revert in misunderstanding and di culties to work collaboratively. To address this issue it is proposed to ll the gap between the data and its acquisition through a logging system named EARS that records underway data such as for example: meteo or swell, and events such as: anomalies or acquisition milestones. Once all these information are stored, they can be linked to the data through an OGC compliant metadata model and gathered as summaries as required by several data management initiatives.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.14083/3645
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