| Title |
Data distribution in public veterinary service: health and safety challenges push for context-aware systems
|
|---|---|
| Published in |
BMC Veterinary Research, December 2017
|
| DOI | 10.1186/s12917-017-1320-0 |
| Pubmed ID | |
| Authors |
Laura Contalbrigo, Stefano Borgo, Giandomenico Pozza, Stefano Marangon |
| Abstract |
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. 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). 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 20% |
| Student > Doctoral Student | 4 | 16% |
| Researcher | 4 | 16% |
| Student > Postgraduate | 2 | 8% |
| Student > Ph. D. Student | 2 | 8% |
| Other | 4 | 16% |
| Unknown | 4 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 36% |
| Veterinary Science and Veterinary Medicine | 3 | 12% |
| Business, Management and Accounting | 2 | 8% |
| Agricultural and Biological Sciences | 2 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Other | 4 | 16% |
| Unknown | 4 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 January 2018.
All research outputs
#18,579,736
of 23,012,811 outputs
Outputs from BMC Veterinary Research
#1,931
of 3,064 outputs
Outputs of similar age
#329,007
of 440,933 outputs
Outputs of similar age from BMC Veterinary Research
#82
of 106 outputs
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