Bayesian Clustering by Dynamics
Machine Learning - Special issue: Unsupervised learning
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
InteMon: continuous mining of sensor data in large-scale self-infrastructures
ACM SIGOPS Operating Systems Review
InteMon: intelligent system monitoring on large clusters
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Tempest: A portable tool to identify hot spots in parallel code
ICPP '07 Proceedings of the 2007 International Conference on Parallel Processing
On building next generation data centers: energy flow in the information technology stack
COMPUTE '08 Proceedings of the 1st Bangalore Annual Compute Conference
Sustainable operation and management of data center chillers using temporal data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Alert systems for production plants: a methodology based on conflict analysis
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Sustainable ecosystems: enabled by supply and demand management
ICDCN'11 Proceedings of the 12th international conference on Distributed computing and networking
Temporal data mining approaches for sustainable chiller management in data centers
ACM Transactions on Intelligent Systems and Technology (TIST)
Towards a net-zero data center
ACM Journal on Emerging Technologies in Computing Systems (JETC)
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We present a data mining approach to model the cooling infrastructure in data centers, particularly the chiller ensemble. These infrastructures are poorly understood due to the lack of “first principles” models of chiller systems. At the same time, they abound in data due to instrumentation by modern sensor networks. We present a multi-level framework to transduce sensor streams into an actionable dynamic Bayesian network model of the system. This network is then used to explain observed system transitions and aid in diagnostics and prediction. We showcase experimental results using a HP data center in Bangalore, India.