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
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
ACM SIGKDD Explorations Newsletter
Failure Prediction in IBM BlueGene/L Event Logs
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Proceedings of the VLDB Endowment
Online discovery and maintenance of time series motifs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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)
ThermoCast: a cyber-physical forecasting model for datacenters
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining for modeling chiller systems in data centers
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Visual exploration of frequent patterns in multivariate time series
Information Visualization - Special issue on Visualization and Data Analysis 2011
RainMon: an integrated approach to mining bursty timeseries monitoring data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards a net-zero data center
ACM Journal on Emerging Technologies in Computing Systems (JETC)
A visual analytics approach for peak-preserving prediction of large seasonal time series
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Mining characteristic multi-scale motifs in sensor-based time series
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Motivation: Data centers are a critical component of modern IT infrastructure but are also among the worst environmental offenders through their increasing energy usage and the resulting large carbon footprints. Efficient management of data centers, including power management, networking, and cooling infrastructure, is hence crucial to sustainability. In the absence of a 'first-principles' approach to manage these complex components and their interactions, data-driven approaches have become attractive and tenable. Results: We present a temporal data mining solution to model and optimize performance of data center chillers, a key component of the cooling infrastructure. It helps bridge raw, numeric, time-series information from sensor streams toward higher level characterizations of chiller behavior, suitable for a data center engineer. To aid in this transduction, temporal data streams are first encoded into a symbolic representation, next run-length encoded segments are mined to form frequent motifs in time series, and finally these metrics are evaluated by their contributions to sustainability. A key innovation in our application is the ability to intersperse "don't care" transitions (e.g., transients) in continuous-valued time series data, an advantage we inherit by the application of frequent episode mining to symbolized representations of numeric time series. Our approach provides both qualitative and quantitative characterizations of the sensor streams to the data center engineer, to aid him in tuning chiller operating characteristics. This system is currently being prototyped for a data center managed by HP and experimental results from this application reveal the promise of our approach.