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» Practical Algorithms for Tracking Database Join Sizes
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PKDD
2010
Springer
235views Data Mining» more  PKDD 2010»
13 years 6 months ago
Online Structural Graph Clustering Using Frequent Subgraph Mining
The goal of graph clustering is to partition objects in a graph database into different clusters based on various criteria such as vertex connectivity, neighborhood similarity or t...
Madeleine Seeland, Tobias Girschick, Fabian Buchwa...
IPSN
2007
Springer
14 years 2 months ago
MeshEye: a hybrid-resolution smart camera mote for applications in distributed intelligent surveillance
Surveillance is one of the promising applications to which smart camera motes forming a vision-enabled network can add increasing levels of intelligence. We see a high degree of i...
Stephan Hengstler, Daniel Prashanth, Sufen Fong, H...
OOPSLA
2007
Springer
14 years 2 months ago
Scalable omniscient debugging
Omniscient debuggers make it possible to navigate backwards in time within a program execution trace, drastically improving the task of debugging complex applications. Still, they...
Guillaume Pothier, Éric Tanter, José...
SIGMOD
2010
ACM
243views Database» more  SIGMOD 2010»
13 years 8 months ago
Non-homogeneous generalization in privacy preserving data publishing
Most previous research on privacy-preserving data publishing, based on the k-anonymity model, has followed the simplistic approach of homogeneously giving the same generalized val...
Wai Kit Wong, Nikos Mamoulis, David Wai-Lok Cheung
KDD
2006
ACM
164views Data Mining» more  KDD 2006»
14 years 9 months ago
Sampling from large graphs
Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.)...
Jure Leskovec, Christos Faloutsos