Audit capabilities in pattern recognition systems
Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes.
1. A system, comprising memory and logic operatively coupled to the memory and configured to:
receive a first vector, the first vector representing first training data;
in a learning operation, associate the first vector with a first knowledge element of a knowledge base;
store an association between the first training data and the first knowledge element;
receive a second vector;
in a recognition operation, match the second vector to the first knowledge element; and
retrieve the first training data based on the association between the first training data and the first knowledge element.
2. The system of claim 1 , wherein the logic is configured to retrieve the first training data using an identifier of the first knowledge element.
3. The system of claim 1 , wherein the logic is configured to retrieve the first training data using information associated with the first knowledge element but not processed by the knowledge base.
4. The system of claim 1 , wherein the logic is configured to generate the first knowledge element using the first vector.
5. The system of claim 1 , wherein the logic is configured to retrieve the first training data in response to a request generated by an auditing application.
6. The system of claim 5 , wherein the logic is further configured to present a representation of the first training data in a user interface of the auditing application.
7. The system of claim 6 , wherein the first training data comprises one or more of analog data, digital data, sensor data, one or more database fields, one or more network packets, image data, video data, audio data, chemical data, text data, binary data, or radio frequency data.
8. The system of claim 1 , wherein the first knowledge element is one of a plurality of knowledge elements of the knowledge base, and wherein the logic is further configured to maintain a count for each of the knowledge elements representing a number of matched vectors corresponding to that knowledge element.
9. The system of claim 1 , wherein the first knowledge element is one of a plurality of knowledge elements of the knowledge base, and wherein the logic is further configured to maintain a count for each of the knowledge elements representing a number of times a user-defined rule was satisfied for that knowledge element.
10. The system of claim 1 , wherein the logic is configured to match the second vector to the first knowledge element using one of a radial basis function, a K-nearest-neighbors function, a Bayesian function, a linear function, or an artificial neural network.
11. The system of claim 1 , wherein the logic is further configured to match the second vector to a second knowledge element, and retrieve second training data associated with the second knowledge element.
12. The system of claim 1 , wherein the logic and memory are implemented as one or more computing devices, one or more programmable logic devices, one or more field-programmable gate arrays, or one or more application-specific integrated circuits.
13. A computer-implemented method, comprising:
receiving a first vector, the first vector representing first training data;
in a learning operation, associating the first vector with a first knowledge element of a knowledge base;
storing an association between the first training data and the first knowledge element;
receiving a second vector;
in a recognition operation, matching the second vector to the first knowledge element; and
retrieving the first training data based on the association between the first training data and the first knowledge element.
14. The method of claim 13 , wherein the first training data are retrieved using an identifier of the first knowledge element.
15. The method of claim 13 , wherein the first training data are retrieved using information associated with the first knowledge element but not processed by the knowledge base.
16. The method of claim 13 , wherein associating the first vector with the first knowledge element includes generating the first knowledge element using the first vector.
17. The method of claim 13 , wherein retrieve the first training data are retrieved in response to a request generated by an auditing application.
18. The method of claim 17 , further comprising presenting a representation of the first training data in a user interface of the auditing application.
19. The method of claim 18 , wherein the first training data comprises one or more of analog data, digital data, sensor data, one or more database fields, one or more network packets, image data, video data, audio data, chemical data, text data, binary data, or radio frequency data.
20. The method of claim 13 , wherein the first knowledge element is one of a plurality of knowledge elements of the knowledge base, the method further comprising maintaining a count for each of the knowledge elements representing a number of matched vectors corresponding to that knowledge element.
21. The method of claim 13 , wherein the first knowledge element is one of a plurality of knowledge elements of the knowledge base, the method further comprising maintaining a count for each of the knowledge elements representing a number of times a user-defined rule was satisfied for that knowledge element.
22. The method of claim 13 , wherein the second vector is matched to the first knowledge element using one of a radial basis function, a K-nearest-neighbors function, a Bayesian function, a linear function, or an artificial neural network.
23. The method of claim 13 , further comprising matching the second vector to a second knowledge element, and retrieving second training data associated with the second knowledge element.
24. A computer program product, comprising one or more computer-readable media having computer program instructions stored therein, the computer program instructions being configured such that, when executed by one or more computing devices, the computer program instructions cause the one or more computing devices to:
receive a first vector, the first vector representing first training data;
in a learning operation, associate the first vector with a first knowledge element of a knowledge base;
store an association between the first training data and the first knowledge element;
receive a second vector;
in a recognition operation, match the second vector to the first knowledge element; and
retrieve the first training data based on the association between the first training data and the first knowledge element.