Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
We consider the problem of publishing sensitive transaction data with privacy preservation. High dimensionality of transaction data poses unique challenges on data privacy and dat...
Yabo Xu, Benjamin C. M. Fung, Ke Wang, Ada Wai-Che...
Dwarf is a highly compressed structure for computing, storing, and querying data cubes. Dwarf identifies prefix and suffix structural redundancies and factors them out by coalesci...
Yannis Sismanis, Antonios Deligiannakis, Nick Rous...
Z39.50 is a client/server protocol widely used in digital libraries and museums for searching and retrieving information spread over a number of heterogeneous sources. To overcome ...
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...