Annotating binary decision diagrams representing sensor data

• US 8,645,108 B2
• Filed: 08/17/2010
• Issued: 02/04/2014
• Est. Priority Date: 08/17/2010
• Status: Expired due to Fees

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: Stergiou, Stergios, Jain, Jawahar
• Primary Examiner(s): Craig, Dwin M
• Application Number:
  US12/857,613
• Publication Number:
  US 20120046919A1
• Time in Patent Office: 1,267 Days
• Field of Search: 703/2, 706/59
• US Class Current: 703/2
• CPC Class Codes: G06F 16/20 of structured data, e.g. relational data

Abstract

According to certain embodiments, one or more sets of model samples of model sensor data are accessed. Each set comprises one or more model samples corresponding to an annotation of one or more annotations. The following are performed for each set to yield one or more annotated model characteristic functions: represent each model sample of the each set as a model minterm to yield a set of model minterms; generate a model characteristic function from the set of model minterms, the model characteristic function indicating whether a given minterm is a member of the set of model minterms; and annotate the model characteristic function to yield an annotated model characteristic function. A general model characteristic function is generated from the one or more annotated model characteristic functions.

Claims

1. A method comprising:

accessing one or more sets of model samples of model sensor data, each set comprising one or more model samples corresponding to an annotation of one or more annotations;

performing the following for each set to yield one or more annotated model characteristic functions;

representing each model sample of the each set as a model minterm to yield a set of model minterms;

generating a model characteristic function from the set of model minterms, the model characteristic function indicating whether a given minterm is a member of the set of model minterms; and

annotating the model characteristic function to yield an annotated model characteristic function; and

generating a general model characteristic function from the one or more annotated model characteristic functions.

2. The method of claim 1, the annotating the model characteristic function further comprising:

taking a product of a Boolean variable and the model characteristic function to yield the annotated model characteristic function, the Boolean variable representing the annotation corresponding to the each set.

3. The method of claim 1, the generating the general model characteristic further comprising:

applying a logical OR operation to the one or more annotated model characteristic functions to yield the general model characteristic function.

4. The method of claim 1, further comprising:

receiving a characteristic function representing a plurality of samples of sensor data, the sensor data recording a plurality of measurements taken by one or more sensors; and

annotating the plurality of samples using the general model characteristic function.

5. The method of claim 1, further comprising:

receiving a characteristic function representing a plurality of samples of sensor data, the sensor data recording a plurality of measurements taken by one or more sensors; and

applying a product function to the characteristic function and the general model characteristic function to annotate the plurality of samples.

6. The method of claim 1, each model sample comprising a tuple of one or more model sensor values, each model sensor value representing one or more measurements could be taken by one or more sensors.

7. The method of claim 1, the model sensor data representing a plurality of measurements taken by one or more sensors.

8. An apparatus comprising:

a memory configured to store one or more sets of model samples of model sensor data, each set comprising one or more model samples corresponding to an annotation of one or more annotations; and

one or more processors configured to;

perform the following for each set to yield one or more annotated model characteristic functions;

represent each model sample of the each set as a model minterm to yield a set of model minterms;

generate a model characteristic function from the set of model minterms, the model characteristic function indicating whether a given minterm is a member of the set of model minterms; and

annotate the model characteristic function to yield an annotated model characteristic function; and

generate a general model characteristic function from the one or more annotated model characteristic functions.

9. The apparatus of claim 8, the annotating the model characteristic function further comprising:

taking a product of a Boolean variable and the model characteristic function to yield the annotated model characteristic function, the Boolean variable representing the annotation corresponding to the each set.

10. The apparatus of claim 8, the generating the general model characteristic further comprising:

applying a logical OR operation to the one or more annotated model characteristic functions to yield the general model characteristic function.

11. The apparatus of claim 8, the one or more processors further configured to:

receive a characteristic function representing a plurality of samples of sensor data, the sensor data recording a plurality of measurements taken by one or more sensors; and

annotate the plurality of samples using the general model characteristic function.

12. The apparatus of claim 8, the one or more processors further configured to:

receive a characteristic function representing a plurality of samples of sensor data, the sensor data recording a plurality of measurements taken by one or more sensors; and

apply a product function to the characteristic function and the general model characteristic function to annotate the plurality of samples.

13. The apparatus of claim 8, each model sample comprising a tuple of one or more model sensor values, each model sensor value representing one or more measurements could be taken by one or more sensors.

14. The apparatus of claim 8, the model sensor data representing a plurality of measurements taken by one or more sensors.

15. One or more non-transitory computer-readable media storing code, when executed by one or more processors, configured to:

access one or more sets of model samples of model sensor data, each set comprising one or more model samples corresponding to an annotation of one or more annotations; and

perform the following for each set to yield one or more annotated model characteristic functions;

represent each model sample of the each set as a model minterm to yield a set of model minterms;

generate a model characteristic function from the set of model minterms, the model characteristic function indicating whether a given minterm is a member of the set of model minterms; and

annotate the model characteristic function to yield an annotated model characteristic function; and

generate a general model characteristic function from the one or more annotated model characteristic functions.

16. The media of claim 15, the annotating the model characteristic function further comprising:

taking a product of a Boolean variable and the model characteristic function to yield the annotated model characteristic function, the Boolean variable representing the annotation corresponding to the each set.

17. The media of claim 15, the generating the general model characteristic further comprising:

applying a logical OR operation to the one or more annotated model characteristic functions to yield the general model characteristic function.

18. The media of claim 15, the code further configured to:

receive a characteristic function representing a plurality of samples of sensor data, the sensor data recording a plurality of measurements taken by one or more sensors; and

annotate the plurality of samples using the general model characteristic function.

19. The media of claim 15, the code further configured to:

receive a characteristic function representing a plurality of samples of sensor data, the sensor data recording a plurality of measurements taken by one or more sensors; and

apply a product function to the characteristic function and the general model characteristic function to annotate the plurality of samples.

20. The media of claim 15, each model sample comprising a tuple of one or more model sensor values, each model sensor value representing one or more measurements could be taken by one or more sensors.

21. The media of claim 15, the model sensor data representing a plurality of measurements taken by one or more sensors.