DATA ANALYTIC ENGINE TOWARDS THE SELF-MANAGEMENT OF COMPLEX PHYSICAL SYSTEMS

• US 20140108324A1
• Filed: 10/10/2013
• Published: 04/17/2014
• Est. Priority Date: 10/12/2012
• Status: Abandoned Application

• Current Assignee: NEC Laboratories America Inc (NEC Corporation)
• Original Assignee: NEC Laboratories America Inc (NEC Corporation)
• Inventors: Chen, Haifeng, Ding, Min, Liu, Bin, Sharma, Abhishek, Yoshihira, Kenji, Jiang, Guofei
• Application Number:
  US14/050,808
• Publication Number:
  US 20140108324A1
• Time in Patent Office: Days
• US Class Current: 706/52
• CPC Class Codes: G06F 11/3055 Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available error or fault processing without redundancy G06F11/0703; error detection or correction by redundancy in data representation G06F11/08; error detection or correction by redundancy in operation G06F11/14; error detection or correction by redundancy in hardware G06F11/16
• CPC Class Codes: G06F 11/3072 where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting
• CPC Class Codes: G06F 17/16 Matrix or vector computation , e.g. matrix-matrix or matrix-vector multiplication, matrix factorization matrix transposition G06F7/78
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/048 Fuzzy inferencing

Abstract

Systems and method for modeling system dynamics, including extracting features representative of a temporal evolution of a dynamical system, further including deriving one or more vector trajectories by performing sliding window segmentation of one or more time series; applying a linear test to determine whether the one or more vector trajectories are linear or nonlinear; and performing linear or nonlinear subspace decomposition on the vector trajectory based on the linear test. The system and method may generate a system evolution model from the extracted features of the dynamical system and determine a fitness score of the system evolution model.

Claims

1. A method for modeling system dynamics, comprising:

extracting features representative of a temporal evolution of a dynamical system, including;

deriving one or more vector trajectories by performing sliding window segmentation of one or more time series;

applying a linear test to determine whether the one or more vector trajectories are linear or nonlinear; and

performing linear or nonlinear subspace decomposition on the vector trajectory based on the linear test; and

generating, using a processor, a system evolution model from the extracted features of the dynamical system; and

determining a fitness score of the system evolution model.

2. The method as recited in claim 1, wherein the dynamical system is a deterministic system.

3. The method as recited in claim 1, wherein the system evolution model is constructed using a Vector-Autoregressive (VAR) technique.

4. The method as recited in claim 1, wherein the system evolution model is constructed using a density based approach for a time series which lack smoothness in the one or more vector trajectories.

5. The method as recited in claim 1, wherein the system evolution model monitors a current system status and compares the current system status with the system evolution model to detect anomalies.

6. The method as recited in claim 1, wherein a window size is increased during the sliding window segmentation to improve detection precision.

7. A system for modeling system dynamics, comprising:

a processor configured to extract features representative of a temporal evolution of a dynamical system, including;

a sliding window segmentation module configured to derive one or more vector trajectories of one or more time series;

a linear testing module configured to determine whether the one or more vector trajectories are linear or nonlinear; and

a subspace decomposition module configured to perform linear or nonlinear subspace decomposition on the vector trajectory based on the linear test; and

a modeling module configured to generate a system evolution model from the extracted features of the dynamical system; and

an analytic engine configured to determine a fitness score of the system evolution model.

8. The system as recited in claim 7, wherein the dynamical system is a deterministic system.

9. The system as recited in claim 7, wherein the system evolution model is constructed using a Vector-Autoregressive (VAR) technique.

10. The system as recited in claim 7, wherein the system evolution model is constructed using a density based approach for a time series which lack smoothness in the one or more vector trajectories.

11. The system as recited in claim 7, wherein the system evolution model monitors a current system status and compares the current system status with the system evolution model to detect anomalies.

12. The system as recited in claim 7, wherein a window size is increased during the sliding window segmentation to improve detection precision.