@article{(Open Science Index):https://publications.waset.org/pdf/10010895,
	  title     = {A Bayesian Classification System for Facilitating an Institutional Risk Profile Definition},
	  author    = {Roman Graf and  Sergiu Gordea and  Heather M. Ryan},
	  country	= {},
	  institution	= {},
	  abstract     = {This paper presents an approach for easy creation and
classification of institutional risk profiles supporting endangerment
analysis of file formats. The main contribution of this work is the
employment of data mining techniques to support set up of the most
important risk factors. Subsequently, risk profiles employ risk factors
classifier and associated configurations to support digital preservation
experts with a semi-automatic estimation of endangerment group
for file format risk profiles. Our goal is to make use of an expert
knowledge base, accuired through a digital preservation survey
in order to detect preservation risks for a particular institution.
Another contribution is support for visualisation of risk factors for
a requried dimension for analysis. Using the naive Bayes method,
the decision support system recommends to an expert the matching
risk profile group for the previously selected institutional risk profile.
The proposed methods improve the visibility of risk factor values
and the quality of a digital preservation process. The presented
approach is designed to facilitate decision making for the preservation
of digital content in libraries and archives using domain expert
knowledge and values of file format risk profiles. To facilitate
decision-making, the aggregated information about the risk factors
is presented as a multidimensional vector. The goal is to visualise
particular dimensions of this vector for analysis by an expert and
to define its profile group. The sample risk profile calculation and
the visualisation of some risk factor dimensions is presented in the
evaluation section.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {13},
	  number    = {11},
	  year      = {2019},
	  pages     = {583 - 590},
	  ee        = {https://publications.waset.org/pdf/10010895},
	  url   	= {https://publications.waset.org/vol/155},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 155, 2019},
	}