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Efficient management of data is difficult in a manual system, and often involves duplication of efforts and wastage of time.
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The efficient management of monitoring data is necessary for large geotechnical engineering projects.
However, the efficient management of these data has remained a goal.
To cope with this, we propose a new product line architecture (PLA) based scientific solution, the Ant-App-DB; a user friendly, smart phone and tablet application, helpful in efficient management of experimental data including location, date, time, geographical measurements, feeders, registered and unregistered ants.
The present research paper proposed SoA-Mist i.e. a three-tier secure framework for efficient management of geospatial health data with the use of mist devices.
Our work presages a time when emerging technologies facilitate inexpensive capture and efficient management of point cloud data, thus enabling visual and analytical tools for enhanced discharge planning, new insights for designers of consumer-facing clinical informatics solutions, and a robust approach to context-based studies of health-related work environments.
One of the solutions is the efficient management of the clinical data regarding the treatment of the patients and epidemiological analyses.
The Washington State University apple breeding program (WABP) is an example of a modern breeding program requiring efficient management of a large data volume from diverse sources.
Over the years this software has proved that it is able to respond to the need for efficient management of the clinical data and organization of the health centres.
The efficient management of this type of data must rely on uniform network overlays.
Efficient management of the vast array of data generated poses many challenges.
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