Performance testing based on variable length segmentation and clustering of time series data
First Claim
1. A device comprising:
- a processor; and
a memory storing instructions that are executable to cause the processor to;
receive training time series data from a system of interest,segment the training time series data into segments of homogeneous windows of variable lengths, wherein each segment of the segments of homogeneous windows of variable lengths is a segment of the training time series data that contains values that are comparable with each other within a predetermined threshold of deviation,cluster the segments into clusters,associate each cluster of the clusters with a cluster score,generate a trained model including the clusters and the cluster scores,receive testing time series data from the system of interest,determine a performance metric for the testing time series data by analyzing the testing time series data based on the trained model, andgenerate, for display, the performance metric on an interactive graphical user interface.
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Accused Products
Abstract
Performance testing based on variable length segmentation and clustering of time series data is disclosed. One example is a system including a training module, a performance testing module, and an interface module. The training module generates a trained model to learn characteristics of a system of interest from training time series data by segmenting the training time series data into homogeneous windows of variable length, clustering the segments to identify patterns, and associating each cluster with a cluster score. The performance testing module analyzes system characteristics from testing time series data by receiving the testing time series data, and determining a performance metric for the testing time series data by analyzing the testing time series data based on the trained model. The interface module is communicatively linked to the performance testing module, and provides the performance metric via an interactive graphical user interface.
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Citations
18 Claims
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1. A device comprising:
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a processor; and a memory storing instructions that are executable to cause the processor to; receive training time series data from a system of interest, segment the training time series data into segments of homogeneous windows of variable lengths, wherein each segment of the segments of homogeneous windows of variable lengths is a segment of the training time series data that contains values that are comparable with each other within a predetermined threshold of deviation, cluster the segments into clusters, associate each cluster of the clusters with a cluster score, generate a trained model including the clusters and the cluster scores, receive testing time series data from the system of interest, determine a performance metric for the testing time series data by analyzing the testing time series data based on the trained model, and generate, for display, the performance metric on an interactive graphical user interface. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method comprising:
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receiving training time series data of a system; segmenting, by a processor, the training time series data into segments of homogeneous windows of variable lengths, wherein each of the segments of homogeneous windows of variable lengths is a segment of the training time series data that contains values that are comparable with each other within a predetermined threshold of deviation; clustering, by the processor, the segments into training clusters; associating, by the processor, each of the training clusters with a cluster score; generating, by the processor, a trained model including the training clusters and the cluster scores; receiving testing time series data from the system; determining, by the processor, a performance metric for the testing time series data based on the trained model; and providing the performance metric via an interactive graphical user interface. - View Dependent Claims (11, 12, 13, 14)
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15. A non-transitory computer readable medium comprising executable instructions to cause a processor to:
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receive training time series data from a plurality of sources; segment the training time series data into segments of homogeneous windows of variable lengths, wherein each segment of the segments of homogeneous windows of variable lengths is a segment of the training time series data that contains values that are comparable with each other within a predetermined threshold of deviation; cluster the segments into training clusters; associate each of the training clusters with a cluster score; generate a trained model including the training clusters and the cluster scores; receive testing time series data from a system of interest; determine a performance metric for the testing time series data by analyzing the testing time series data based on the trained model; and provide the performance metric via an interactive graphical user interface. - View Dependent Claims (16, 17, 18)
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Specification