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Data distribution in public veterinary service: health and safety challenges push for context-aware systems
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Data distribution in public veterinary service: health and safety challenges push for context-aware systems
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Journal Article
Data distribution in public veterinary service: health and safety challenges push for context-aware systems
2017
Overview
Background
Today’s globalised and interconnected world is characterized by intertwined and quickly evolving relationships between animals, humans and their environment and by an escalating number of accessible data for public health. The public veterinary services must exploit new modeling and decision strategies to face these changes. The organization and control of data flows have become crucial to effectively evaluate the evolution and safety concerns of a given situation in the territory. This paper discusses what is needed to develop modern strategies to optimize data distribution to the stakeholders.
Main text
If traditionally the system manager and knowledge engineer have been concerned with the increase of speed of data flow and the improvement of data quality, nowadays they need to worry about data overflow as well. To avoid this risk an information system should be capable of selecting the data which need to be shown to the human operator. In this perspective, two aspects need to be distinguished: data classification vs data distribution. Data classification is the problem of organizing data depending on what they refer to and on the way they are obtained; data distribution is the problem of selecting which data is accessible to which stakeholder. Data classification can be established and implemented via ontological analysis and formal logic but we claim that a context-based selection of data should be integrated in the data distribution application. Data distribution should provide these new features: (a) the organization of situation types distinguishing at least ordinary vs extraordinary scenarios (
contextualization of scenarios
); (b) the possibility to focus on the data that are really important in a given scenario (
data contextualization by scenarios
); and (c) the classification of which data is relevant to which stakeholder (
data contextualization by users
).
Short conclusion
Public veterinary services, to efficaciously and efficiently manage the information needed for today’s health and safety challenges, should contextualize and filter the continuous and growing flow of data by setting suitable frameworks to classify data, users’ roles and possible situations.
Publisher
BioMed Central,BioMed Central Ltd,BMC
