28,495 research outputs found
Design and initial validation of the Raster method for telecom service availability risk assessment
Crisis organisations depend on telecommunication services; unavailability of these services reduces the effectiveness of crisis response. Crisis organisations should therefore be aware of availability risks, and need a suitable risk assessment method. Such a method needs to be aware of the exceptional circumstances in which crisis organisations operate, and of the commercial structure of modern telecom services. We found that existing risk assessment methods are unsuitable for this problem domain. Hence, crisis organisations do not perform any risk assessment, trust their supplier, or rely on service level agreements, which are not meaningful during crisis situations. We have therefore developed a new risk assessment method, which we call RASTER. We have tested RASTER using a case study at the crisis organisation of a government agency, and improved the method based on the analysis of case results. Our initial validation suggests that the method can yield practical results
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OntoEng: A design method for ontology engineering in information systems
This paper addresses the design problem relating to ontology engineering in the discipline of information systems. Ontology engineering is a realm that covers issues related to ontology development and use throughout its life span. Nowadays, ontology as a new innovation promises to improve the design, semantic integration, and utilization of information systems. Ontologies are the backbone of knowledge-based systems. In addition, they establish sharable and reusable common understanding of specific domains amongst people, information systems, and software agents. Notwithstanding, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. On the basis of the
gathered experience during the development of V4 Telecoms Business Model Ontology as well as the conducted integration of the related literature from the design science paradigm, this paper introduces OntoEng and its application as a novel systematic design
method for ontology engineering
A Study on Clustering for Clustering Based Image De-Noising
In this paper, the problem of de-noising of an image contaminated with
Additive White Gaussian Noise (AWGN) is studied. This subject is an open
problem in signal processing for more than 50 years. Local methods suggested in
recent years, have obtained better results than global methods. However by more
intelligent training in such a way that first, important data is more effective
for training, second, clustering in such way that training blocks lie in
low-rank subspaces, we can design a dictionary applicable for image de-noising
and obtain results near the state of the art local methods. In the present
paper, we suggest a method based on global clustering of image constructing
blocks. As the type of clustering plays an important role in clustering-based
de-noising methods, we address two questions about the clustering. The first,
which parts of the data should be considered for clustering? and the second,
what data clustering method is suitable for de-noising.? Then clustering is
exploited to learn an over complete dictionary. By obtaining sparse
decomposition of the noisy image blocks in terms of the dictionary atoms, the
de-noised version is achieved. In addition to our framework, 7 popular
dictionary learning methods are simulated and compared. The results are
compared based on two major factors: (1) de-noising performance and (2)
execution time. Experimental results show that our dictionary learning
framework outperforms its competitors in terms of both factors.Comment: 9 pages, 8 figures, Journal of Information Systems and
Telecommunications (JIST
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