Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
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
Modeling changing dependency structure in multivariate time series
Proceedings of the 24th international conference on Machine learning
Detecting time series motifs under uniform scaling
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
On building next generation data centers: energy flow in the information technology stack
COMPUTE '08 Proceedings of the 1st Bangalore Annual Compute Conference
LiveRAC: interactive visual exploration of system management time-series data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Stream prediction using a generative model based on frequent episodes in event sequences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Toward Predictive Failure Management for Distributed Stream Processing Systems
ICDCS '08 Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems
Failure Prediction in IBM BlueGene/L Event Logs
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Knowledge and Information Systems
Proceedings of the VLDB Endowment
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
Detecting large-scale system problems by mining console logs
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
SysML'08 Proceedings of the Third conference on Tackling computer systems problems with machine learning techniques
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
Data mining for modeling chiller systems in data centers
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
A finite state machine-based characterization of building entities for monitoring and control
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Strip, bind, and search: a method for identifying abnormal energy consumption in buildings
Proceedings of the 12th international conference on Information processing in sensor networks
Editorial: Pattern-growth based frequent serial episode discovery
Data & Knowledge Engineering
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Practically every large IT organization hosts data centers---a mix of computing elements, storage systems, networking, power, and cooling infrastructure---operated either in-house or outsourced to major vendors. A significant element of modern data centers is their cooling infrastructure, whose efficient and sustainable operation is a key ingredient to the “always-on” capability of data centers. We describe the design and implementation of CAMAS (Chiller Advisory and MAnagement System), a temporal data mining solution to mine and manage chiller installations. CAMAS embodies a set of algorithms for processing multivariate time-series data and characterizes sustainability measures of the patterns mined. We demonstrate three key ingredients of CAMAS---motif mining, association analysis, and dynamic Bayesian network inference---that help bridge the gap between low-level, raw, sensor streams, and the high-level operating regions and features needed for an operator to efficiently manage the data center. The effectiveness of CAMAS is demonstrated by its application to a real-life production data center managed by HP.