@inbook {19607,
	title = {Effective Visualization of File System Access-Control},
	booktitle = {Visualization for Computer Security},
	series = {Lecture Notes in Computer Science},
	year = {2008},
	month = {2008/01/01/},
	pages = {18 - 25},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	abstract = {In this paper, we present a visual representation of access control permissions in a standard hierarchical file system. Our visualization of file permissions leverages treemaps, a popular graphical representation of hierarchical data. In particular, we present a visualization of access control for the NTFS file system that can help a non-expert user understand and manipulate file system permissions in a simple and effective way. While our examples are based on NTFS, our approach can be used for many other hierarchical file systems as well.},
	keywords = {Computer Communication Networks, Computer Imaging, Vision, Pattern Recognition and Graphics, data mining and knowledge discovery, Data Structures, Cryptology and Information Theory, Visualization},
	isbn = {978-3-540-85931-4, 978-3-540-85933-8},
	url = {http://link.springer.com/chapter/10.1007/978-3-540-85933-8_2},
	author = {Heitzmann, Alexander and Palazzi, Bernardo and Charalampos Papamanthou and Tamassia, Roberto},
	editor = {Goodall, John R. and Conti, Gregory and Ma, Kwan-Liu}
}
@conference {13480,
	title = {Visualization of high-dimensional model characteristics},
	booktitle = {Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management},
	series = {NPIVM {\textquoteright}99},
	year = {1999},
	month = {1999///},
	pages = {6 - 8},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Using inductive learning techniques to construct explanatory models for large, high-dimensional data sets is a useful way to discover useful information. However, these models can be difficult for users to understand. We have developed a set of visualization methods that enable a user to evaluate the quality of learned models, to compare alternative models, and identify ways in which a model might be improved We describe the visualization techniques we have explored, including methods for high-dimensional data space projection, variable/class correlation, instance mapping, and model sampling We show the results of applying these techniques to several models built from a benchmark data set of census data.},
	keywords = {data mining and knowledge discovery, multidimensional information spaces, Visualization},
	isbn = {1-58113-254-9},
	doi = {10.1145/331770.331774},
	url = {http://doi.acm.org/10.1145/331770.331774},
	author = {desJardins, Marie and Rheingans,Penny}
}
