PERFORMANCE TESTING BASED ON VARIABLE LENGTH SEGMENTATION AND CLUSTERING OF TIME SERIES DATA
First Claim
1. A system comprising:
- a training module to generate 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, andassociating each cluster with a cluster score;
a performance testing module to analyze system characteristics from testing time series data by;
receiving the testing time series data, anddetermining a performance metric for the testing time series data by analyzing the testing time series data based on the trained model; and
an interface module, communicatively linked to the performance testing module, to provide the performance metric via 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
15 Claims
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1. A system comprising:
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a training module to generate 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; a performance testing module to analyze 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; and an interface module, communicatively linked to the performance testing module, to provide the performance metric via an interactive graphical user interface. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for determining a performance metric for a system, the method comprising:
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pre-processing, via a processor, testing time series data related to a vehicular driving record under performance testing; segmenting the testing time series data based on windows of variable length to determine homogeneous time windows; associating each segment with a training cluster in a trained model, the trained model generated based on training time series data; determining a segment score for each segment based on a cluster score for the associated training cluster; determining a performance metric for the testing time series data, the performance metric based on a weighted aggregate of the segment scores, and the performance metric indicative of a quality of driving; 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:
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pre-process training time series data received from a plurality of sources; generate a trained model based on the pre-processed training time series data by; segmenting, via a processing system, the pre-processed training time series data into homogeneous windows of variable length, clustering the segments to identify patterns, and associating each cluster with a cluster score; receive testing time series data; determine a performance metric for 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.
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Specification