Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Symbolic representation and retrieval of moving object trajectories
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
VizTree: a tool for visually mining and monitoring massive time series databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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
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
Multi-resolution techniques for visual exploration of large time-series data
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Better drilling through sensor analytics: a case study in live operational intelligence
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
An application of sensor and streaming analytics to oil production
Proceedings of the 17th International Conference on Management of Data
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The detection of frequently occurring patterns, also called motifs, in data streams has been recognized as an important task. To find these motifs, we use an advanced event encoding and pattern discovery algorithm. As a large time series can contain hundreds of motifs, there is a need to support interactive analysis and exploration. In addition, for certain applications, such as data center resource management, service managers want to be able to predict the next day's power consumption from the previous months' data. For this purpose, we introduce four novel visual analytics methods: {i} motif layout - using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs; {ii} motif distortion - enlarging or shrinking motifs for visualizing them more clearly; {iii} motif merging - combining a number of identical adjacent motif instances to simplify the display; and {iv} pattern preserving prediction - using a pattern-preserving smoothing and prediction algorithm to provide a reliable prediction for seasonal data. We have applied these methods to three real-world datasets: data center chilling utilization, oil well production, and system resource utilization. The results enable service managers to interactively examine motifs and gain new insights into the recurring patterns to analyze system operations. Using the above methods, we have also predicted both power consumption and server utilization in data centers with an accuracy of 70-80%.