AUTOMATED CREATION OF SEMANTICALLY-ENRICHED DIAGNOSIS MODELS

• US 20150186777A1
• Filed: 12/30/2013
• Published: 07/02/2015
• Est. Priority Date: 12/30/2013
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Ploennigs, Joern, Schumann, Anika, Lecue, Freddy
• Granted Patent: US 9,679,248 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06N 5/025 Extracting rules from data

Abstract

A system, method and a computer program product may be provided for automatically creating and parameterizing a semantically-enriched diagnosis model for an entity. The system receives a list of data points, from sensors or a database, to be used to create a diagnosis model. The system automatically creates the diagnosis model based on the received list of data points and data stored in a database and parameterizes the diagnosis model. The parameterized diagnosis model reflects rules that determine one or more potential causes of one or more abnormalities of one or more physical conditions in the entity.

Claims

8. A method for automatically creating a parameterized, semantically-enriched diagnosis model, the method comprising:

receiving a historical or real-time time series data, from sensors or a database, associated with a diagnosis model;

creating, based on the received data, the diagnosis model;

parameterizing the diagnosis model, the parameterized diagnosis model mapping one or more potential causes or actual causes to a condition of the system and enabling, based on the mapping, a user or system to identify the one or more potential causes or the actual causes of the condition of the system;

wherein a processor coupled to a memory device perform the receiving, the creating, and the parameterizing.

9. The method according to claim 8, wherein the determining the relationship includes:

analyzing the real-time time series data to extract parameters of the diagnosis model using statistical or data mining.

10. An apparatus for automatically deriving a semantically enriched diagnosis model from a list of data points, the apparatus comprising:

a memory device;

a processor coupled to the memory device,

wherein the processor is configured to perform;

receiving a list of data points, from sensors or a database, to be used to create a diagnosis model; and

automatically creating the diagnosis model based on the received list of data points, the automatically created diagnosis model reflecting rules that determine one or more potential causes of one or more abnormalities associated with one or more conditions in a system.

11. The apparatus according to claim 10, wherein the processor is configured to perform:

semantically annotating the data points list.

12. The apparatus according to claim 10, wherein the processor is configured to perform:

automatically creating semantic variables having values that represent conditions in the system.

13. The apparatus according to claim 10, wherein the processor is configured to perform:

automatically deriving relationships between the variables.

14. The apparatus according to claim 10, wherein the processor is configured to perform:

creating semantic variables that represent the normal and abnormal conditions of the system, the created model computing the rules based on the created variables.

15. The apparatus according to claim 10, wherein the processor is configured to perform:

determining a relationship between the created semantic variables and a dependency of the one or more potential causes to the abnormalities of the one or more conditions.

16. The system of claim 10, further comprising:

providing and using a database that stores a plurality of pre-determined semantic concepts and rules.

17. A apparatus for automatically creating a parameterized, semantically-enriched diagnosis model, the apparatus comprising:

a memory device;

a processor coupled to the memory device,

wherein the processor is configured to perform;

receiving a historical or real-time time series data, from sensors or a database, associated with a diagnosis model;

parameterizing the diagnosis model, the parameterized diagnosis model mapping one or more potential causes or actual causes to a condition of the system and enabling, based on the mapping, a user or system to identify the one or more potential causes or the actual causes of the condition of the system.

18. The apparatus according to claim 17, wherein the determining the relationship includes:

analyzing the real-time time series data to extract parameters of the diagnosis model using statistical or data mining.

19. A computer program product for automatically deriving a semantically enriched diagnosis model from a list of data points, the computer program product comprising a storage medium that excludes a propagating signal, the storage medium readable by a processing circuit and storing instructions run by the processing circuit for performing a method, said method steps comprising:

receiving a list of data points, from sensors or a database, to be used to create a diagnosis model; and

automatically creating the diagnosis model based on the received list of data points, the automatically created diagnosis model reflecting rules that determine one or more potential causes of one or more abnormalities associated with one or more physical conditions in the system.

1. A method for automatically deriving a semantically enriched diagnosis model from a list of data points:

receiving a list of data points, from sensors or a database, to be used to create a diagnosis model; and

automatically creating the diagnosis model based on the received list of data points, the automatically created diagnosis model reflecting rules that determine one or more potential causes of one or more abnormalities associated with one or more conditions in a system,

wherein a processor coupled to a memory device performs the receiving and the automatically creating the diagnosis model.

2. The method according to claim 1, further comprising:

semantically annotating the data points list.

3. The method according to claim 1, further comprising:

automatically creating semantic variables having values that represent conditions in the system.

4. The method according to claim 1, further comprising:

automatically deriving relationships between the variables.

5. The method according to claim 3, further comprising:

creating semantic variables that represent the normal and abnormal conditions of the system, the created model computing the rules based on the created variables.

6. The method according to claim 1, further comprising:

determining a relationship between the created semantic variables and a dependency of the one or more potential causes to the abnormalities of the one or more conditions.

7. The method of claim 1, further comprising:

providing and using the database that stores a plurality of pre-determined semantic concepts and rules.

20. The computer program product according to claim 19, wherein the method steps further comprises:

semantically annotating the data points list.

21. The computer program product according to claim 19, wherein the method steps further comprises:

automatically creating semantic variables having values that represent conditions in the system.

22. The computer program product according to claim 19, wherein the method steps further comprises:

automatically deriving relationships between the variables.

23. The computer program product according to claim 19, further comprising:

providing and using a database that stores a plurality of pre-determined semantic concepts and the rules.

25. The computer program product according to claim 23, wherein the determining the relationship includes:

analyzing the real-time time series data to extract parameters of the diagnosis model using statistical or data mining.

21-1. The computer program product according to claim 19, wherein the method steps further comprise:

creating semantic variables that represent the normal and abnormal conditions of the entity, the created model computing the rules based on the created variables.

22-2. The computer program product according to claim 19, wherein the method steps further includes:

determining a relationship between the created semantic variables and a dependency of the one or more potential causes to the abnormalities of the one or more conditions.

24. A computer program product for automatically creating a parameterized, semantically-enriched diagnosis model, the computer program product comprising a storage medium that excludes a propagating signal, the storage medium readable by a processing circuit and storing instructions run by the processing circuit for performing a method, said method steps comprising:

receiving a historical or real-time time series data, from sensors or a database, associated with a diagnosis model;

creating, based on the received historical or real-time series data, the diagnosis model; and

parameterizing the diagnosis model, the parameterized diagnosis model mapping one or more potential causes or actual causes to a condition of the system and enabling, based on the mapping, a user or system to identify the one or more potential causes or the actual causes of the condition of the system.