This work addresses the need for stateful dataflow programs that can rapidly sift through huge, evolving data sets. These data-intensive applications perform complex multi-step c...
Dionysios Logothetis, Christopher Olston, Benjamin...
The pre-computation of data cubes is critical to improving the response time of On-Line Analytical Processing (OLAP) systems and can be instrumental in accelerating data mining tas...
Ying Chen, Frank K. H. A. Dehne, Todd Eavis, Andre...
The Internet is rapidly changing from a set of wires and switches that carry packets into a sophisticated infrastructure that delivers a set of complex value-added services to end...
Prashant R. Chandra, Allan Fisher, Corey Kosak, T....
A key method for privacy preserving data mining is that of randomization. Unlike k-anonymity, this technique does not include public information in the underlying assumptions. In ...
Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) simi...