At UCLA's Plasma Physics Group, to achieve accessible computational power for our research goals, we developed the tools to build numerically-intensive parallel computing clus...
Large-scale text datasets have long eluded a family of particularly elegant and effective clustering methods that exploits the power of pair-wise similarities between data points ...
Abstract. In this work we present a NIDS cluster as a scalable solution for realizing high-performance, stateful network intrusion detection on commodity hardware. The design addre...
Matthias Vallentin, Robin Sommer, Jason Lee, Craig...
The ultimate goal of data mining is to extract knowledge from massive data. Knowledge is ideally represented as human-comprehensible patterns from which end-users can gain intuiti...
This paper presents a load balancing algorithm for a parallel implementation of an evolutionary strategy on heterogeneous clusters. Evolutionary strategies can efficiency solve a ...