Detecting an event from time-series data sequences
First Claim
1. A method for detecting an event from time-series data sequences, the method comprising:
- receiving, by a processor, the time-series data sequences of variable lengths, generated by a plurality of sensors, wherein the time-series data sequences comprise timestamp information;
sampling the time-series data sequences at a uniform sampling rate for generating sample points;
generating, by the processor, a plurality of pairs of sample points using the sample points, wherein the plurality of pairs of sample points are generated by pairing each sample point with one another;
computing, by the processor, a distance matrix (D) and an angle matrix (A) for one or more pairs of the sample points of the plurality of pairs of sample points;
generating, by the processor, a two-dimensional (2D) shape histogram for the time-series data sequences using the distance matrix (D) and the angle matrix (A), wherein the 2D shape histogram have a constant dimension and the 2D shape histogram represents a global distribution of the plurality of pairs of sample points, wherein the global distribution of the plurality of pairs of sample points is obtained by computing a shape context of each sample point and wherein the shape context of each sample point is defined based on a distance-angle based distribution of each sample point with respect to remaining sample points;
concatenating, by the processor, the 2D shape histograms for one or more time-series data sequences based on the timestamp information to generate a concatenated shape histogram; and
matching, by the processor, the concatenated shape histogram with a pre-stored set of shape histograms to detect an event.
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Abstract
The present subject matter discloses a system and a method for detecting an event from time-series data sequences. The system receives time-series data sequences generated by sensors, wherein the time-series data sequences comprise sample points. The system pairs the sample points with one another for determining pairs of the sample points. The system computes Euclidean distances and angles between the sample points for determining distance matrix and angle matrix corresponding to the sample points. Further, the system determines global distribution of the plurality of pairs of sample points, wherein the global distribution of the plurality of pairs of sample points represent 2D shape histogram for the time-series data sequence. Further, the system concatenates the 2D shape histogram for each time-series data sequence to generate a concatenated shape histogram. Finally the system matches the concatenated shape histogram to pre-stored shape histograms for determining the event.
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Citations
17 Claims
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1. A method for detecting an event from time-series data sequences, the method comprising:
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receiving, by a processor, the time-series data sequences of variable lengths, generated by a plurality of sensors, wherein the time-series data sequences comprise timestamp information; sampling the time-series data sequences at a uniform sampling rate for generating sample points; generating, by the processor, a plurality of pairs of sample points using the sample points, wherein the plurality of pairs of sample points are generated by pairing each sample point with one another; computing, by the processor, a distance matrix (D) and an angle matrix (A) for one or more pairs of the sample points of the plurality of pairs of sample points; generating, by the processor, a two-dimensional (2D) shape histogram for the time-series data sequences using the distance matrix (D) and the angle matrix (A), wherein the 2D shape histogram have a constant dimension and the 2D shape histogram represents a global distribution of the plurality of pairs of sample points, wherein the global distribution of the plurality of pairs of sample points is obtained by computing a shape context of each sample point and wherein the shape context of each sample point is defined based on a distance-angle based distribution of each sample point with respect to remaining sample points; concatenating, by the processor, the 2D shape histograms for one or more time-series data sequences based on the timestamp information to generate a concatenated shape histogram; and matching, by the processor, the concatenated shape histogram with a pre-stored set of shape histograms to detect an event. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system for detecting an event from time-series data sequences, the system comprising:
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a processor; a memory coupled to the processor, wherein the processor is capable for executing a plurality of modules stored in the memory, and wherein the plurality of modules comprising; a pairing module, to receive the time-series data sequences of variable lengths generated by a plurality of sensors, wherein the time-series data sequences comprise timestamp information; to sample the time-series data sequences at a uniform sampling rate for generating sample points; to generate a plurality of pairs of sample points using the sample points such that each sample point is paired with one another;
a matrix computing module,to compute a distance matrix (D) and an angle matrix (A) for one or more pairs of the sample points of the plurality of pairs of sample points; a histogram generating and event determining (HGED) module, to generate a two-dimensional (2D) shape histogram for the time-series data sequences using the distance matrix (D) and the angle matrix (A), wherein the 2D shape histogram have a constant dimension, and the 2D shape histogram represents global distribution of the plurality of pairs of sample points, wherein the global distribution of the plurality of pairs of sample points is obtained by computing a shape context of each sample point and wherein the shape context of each sample point is defined based on a distance-angle based distribution of each sample point with respect to remaining sample points; to concatenate the 2D shape histograms for one or more time-series data sequences based on the timestamp information to generate a concatenated shape histogram; and to match the concatenated shape histogram with a pre-stored set of shape histograms to detect an event. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A non-transitory computer readable medium embodying a program executable in a computing device for detecting an event from time-series data sequences, the program comprising:
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a program code for receiving the time-series data sequences of variable lengths generated by a plurality of sensors, wherein the time-series data sequences comprise timestamp information; a program code for sampling the time-series data sequences at a uniform sampling rate for generating sample points; a program code for generating a plurality of pairs of sample points using the sample points such that each sample point is paired with one another, a program code for computing a distance matrix (D) and an angle matrix (A) for one or more pairs of the sample points of the plurality of pairs of sample points; a program code for generating a two-dimensional (2D) shape histogram for the time-series data sequences using the distance matrix (D) and the angle matrix (A), wherein the 2D shape histogram have a constant dimension, and the 2D shape histogram represents global distribution of the plurality of pairs of sample points, wherein the global distribution of the plurality of pairs of sample points is obtained by computing a shape context of each sample point, wherein the global distribution of the plurality of pairs of sample points is obtained by computing a shape context of each sample point and wherein the shape context of each sample point is defined based on a distance-angle based distribution of each sample point with respect to remaining sample points; a program code for concatenating 2D shape histograms for one or more time-series data sequences based on the timestamp information to generate a concatenated shape histogram; and a program code for matching the concatenated shape histogram with a pre-stored set of shape histograms to detect an event.
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Specification