Temporal data mining approaches for sustainable chiller management in data centers
ACM Transactions on Intelligent Systems and Technology (TIST)
Data mining for modeling chiller systems in data centers
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
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Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human-computer interaction modeling. We focus in this paper on temporal models representable as excitatory networks where all connections are stimulative, rather than inhibitory. Through this emphasis on excitatory networks, we show how they can be learned by creating bridges to frequent episode mining. Specifically, we show that frequent episodes help identify nodes with high mutual information relationships and which can be summarized into a dynamic Bayesian network (DBN). To demonstrate the practical feasibility of our approach, we show how excitatory networks can be inferred from both mathematical models of spiking neurons as well as real neuroscience datasets.