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» Multidimensional Data Modeling for Business Process Analysis
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145
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ICASSP
2011
IEEE
14 years 7 months ago
On selecting the hyperparameters of the DPM models for the density estimation of observation errors
The Dirichlet Process Mixture (DPM) models represent an attractive approach to modeling latent distributions parametrically. In DPM models the Dirichlet process (DP) is applied es...
Asma Rabaoui, Nicolas Viandier, Juliette Marais, E...
138
Voted
DASFAA
2009
IEEE
252views Database» more  DASFAA 2009»
15 years 10 months ago
Optimal Privacy-Aware Path in Hippocratic Databases
Abstract. Privacy becomes a major concern for both customers and enterprises in today’s corporate marketing strategies, many research efforts have been put into developing new p...
Min Li, Xiaoxun Sun, Hua Wang, Yanchun Zhang
163
Voted
GI
1998
Springer
15 years 7 months ago
Self-Organizing Data Mining
"KnowledgeMiner" was designed to support the knowledge extraction process on a highly automated level. Implemented are 3 different GMDH-type self-organizing modeling algo...
Frank Lemke, Johann-Adolf Müller
144
Voted
IPPS
2009
IEEE
15 years 10 months ago
Compact graph representations and parallel connectivity algorithms for massive dynamic network analysis
Graph-theoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socioeconomic interactions, social networking web sites, communication t...
Kamesh Madduri, David A. Bader
VAMOS
2007
Springer
15 years 9 months ago
Exploring the Dimensions of Variability: a Requirements Engineering Perspective
Goal models have been found to be effective for representing and analyzing variability at the early requirements level, by comprehensibly representing all alternative ways by whic...
Sotirios Liaskos, Lei Jiang, Alexei Lapouchnian, Y...