Traditional data quality engineering techniques, often used and deployed within a single enterprise environment, are inadequate to cope with the rapid change of data, with a multit...
Marco Comerio, Hong Linh Truong, Carlo Batini, Sch...
An important problem in data mining is detecting changes in large data sets. Although there are a variety of change detection algorithms that have been developed, in practice it c...
Chris Curry, Robert L. Grossman, David Locke, Stev...
In this paper we propose the integration of Data Mining with Hidden Markov Models when applied to the problem of acoustic bird species recognition. We first show how each of them...
Erika Vilches, Ivan A. Escobar, Edgar E. Vallejo, ...
Software infrastructures and applications more and more must deal with data available in a variety of different storage engines, accessible through a multitude of protocols and in...
Marc Van Cappellen, Wouter Cordewiner, Carlo Innoc...
: We introduce an end-to-end framework for data quality that integrates business strategy, data quality models, and supporting investigative and governance processes. We also descr...