Event Abstractor

• US 20170344404A1
• Filed: 05/24/2016
• Published: 11/30/2017
• Est. Priority Date: 05/24/2016
• Status: Active Grant

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: Liongosari, Edy S.
• Granted Patent: US 10,838,782 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 9/542 Event management; Broadcasting; Multicasting; Notifications

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting higher level events based on lower level events. In one aspect, a method includes receiving a stream model that defines elements of a data domain. A stream mapping that defines sensor identifiers for real-world sensors and associates each sensor identifier with a respective element of the data domain is received. A user-specified stream matching pattern is received. The stream matching pattern specifies respective sensor identifiers of sensor identifiers of the real-world sensors, and for each sensor identifier, a tuple of data elements of the stream model and, for each tuple of data elements, co-occurrence criteria including at least one time window. A stream of events is obtained from the real-world sensors. A determination is made that two or more events co-occur within the time window and whether the one or more co-occurrence criteria are satisfied.

Claims

1. A computer-implemented method comprising:

receiving a stream model that defines a plurality of elements of a data domain;

receiving a stream mapping that defines a sensor identifier for each sensor of a plurality of real-world sensors and associates each sensor identifier with a respective element of the plurality of elements of the data domain, wherein each of the real-world sensors generates a respective stream of real-time data elements received by a stream processing system;

receiving a user-specified stream matching pattern, wherein the stream matching pattern specifies respective sensor identifiers of two or more sensor identifiers of the plurality of real-world sensors, and for each sensor identifier, a tuple of data elements of the stream model and, for each tuple of data elements, one or more co-occurrence criteria, wherein the co-occurrence criteria includes at least one time window;

obtaining a stream of events from the plurality of real-world sensors;

determining that two or more events co-occur within the time window specified by the stream matching pattern;

determining whether the one or more co-occurrence criteria of the stream matching pattern are satisfied; and

whenever the one or more co-occurrence criteria are satisfied, generating an output data element of an output data stream.

2. The method of claim 1, further comprising generating, by a pattern compiler, a plurality of transformations of a stream processing platform.

3. The method of claim 1, wherein the output data element specifies an occurrence of a given event based on the determination that the one or more co-occurrence criteria of the stream matching pattern are satisfied.

4. The method of claim 3, further comprising determining a probability that the given event occurred and including, in the output data element, data specifying the probability.

5. The method of claim 1, wherein determining whether the one or more co-occurrence criteria of the stream matching pattern are satisfied comprises:

detecting, based on a first stream of events received from a first real-world sensor, an occurrence of a first constituent event defined by the user-specified stream matching pattern within the time window;

detecting, based on a second stream of events received from a second real-world sensor different from the first real-world sensor, an occurrence of a second constituent event defined by the user-specified stream matching pattern within the time window; and

determining that the one or more co-occurrence criteria of the stream matching pattern are satisfied based at least on the first constituent event and the second constituent event occurring within the time window.

6. The method of claim 5, wherein:

the stream matching pattern specifies a threshold value for the first constituent event; and

the first constituent event is detected in response to detecting one or more events in the first stream of events that satisfies the threshold value.

7. The method of claim 6, wherein:

the stream matching pattern specifies a threshold amount of time for the first constituent event; and

the first constituent event is detected in response to detecting one or more events in the first stream of events that satisfies the threshold value for at least the threshold amount of time.

8. A system, comprising:

a data processing apparatus; and

a memory storage apparatus in data communication with the data processing apparatus, the memory storage apparatus storing instructions executable by the data processing apparatus and that upon such execution cause the data processing apparatus to perform operations comprising;

receiving a stream model that defines a plurality of elements of a data domain;

receiving a stream mapping that defines a sensor identifier for each sensor of a plurality of real-world sensors and associates each sensor identifier with a respective element of the plurality of elements of the data domain, wherein each of the real-world sensors generates a respective stream of real-time data elements received by a stream processing system;

receiving a user-specified stream matching pattern, wherein the stream matching pattern specifies respective sensor identifiers of two or more sensor identifiers of the plurality of real-world sensors, and for each sensor identifier, a tuple of data elements of the stream model and, for each tuple of data elements, one or more co-occurrence criteria, wherein the co-occurrence criteria includes at least one time window;

obtaining a stream of events from the plurality of real-world sensors;

determining that two or more events co-occur within the time window specified by the stream matching pattern;

determining whether the one or more co-occurrence criteria of the stream matching pattern are satisfied; and

whenever the one or more co-occurrence criteria are satisfied, generating an output data element of an output data stream.

9. The system of claim 8, wherein the operations comprise generating, by a pattern compiler, a plurality of transformations of a stream processing platform.

10. The system of claim 8, wherein the output data element specifies an occurrence of a given event based on the determination that the one or more co-occurrence criteria of the stream matching pattern are satisfied.

11. The system of claim 10, wherein the operations comprise determining a probability that the given event occurred and including, in the output data element, data specifying the probability.

12. The system of claim 8, wherein determining whether the one or more co-occurrence criteria of the stream matching pattern are satisfied comprises:

detecting, based on a first stream of events received from a first real-world sensor, an occurrence of a first constituent event defined by the user-specified stream matching pattern within the time window;

detecting, based on a second stream of events received from a second real-world sensor different from the first real-world sensor, an occurrence of a second constituent event defined by the user-specified stream matching pattern within the time window; and

determining that the one or more co-occurrence criteria of the stream matching pattern are satisfied based at least on the first constituent event and the second constituent event occurring within the time window.

13. The system of claim 12, wherein:

the stream matching pattern specifies a threshold value for the first constituent event; and

the first constituent event is detected in response to detecting one or more events in the first stream of events that satisfies the threshold value.

14. The system of claim 13, wherein:

the stream matching pattern specifies a threshold amount of time for the first constituent event; and

the first constituent event is detected in response to detecting one or more events in the first stream of events that satisfies the threshold value for at least the threshold amount of time.

15. A computer program product, encoded on one or more non-transitory computer storage media, comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

receiving a stream model that defines a plurality of elements of a data domain;

receiving a stream mapping that defines a sensor identifier for each sensor of a plurality of real-world sensors and associates each sensor identifier with a respective element of the plurality of elements of the data domain, wherein each of the real-world sensors generates a respective stream of real-time data elements received by a stream processing system;

receiving a user-specified stream matching pattern, wherein the stream matching pattern specifies respective sensor identifiers of two or more sensor identifiers of the plurality of real-world sensors, and for each sensor identifier, a tuple of data elements of the stream model and, for each tuple of data elements, one or more co-occurrence criteria, wherein the co-occurrence criteria includes at least one time window;

obtaining a stream of events from the plurality of real-world sensors;

determining that two or more events co-occur within the time window specified by the stream matching pattern;

determining whether the one or more co-occurrence criteria of the stream matching pattern are satisfied; and

whenever the one or more co-occurrence criteria are satisfied, generating an output data element of an output data stream.

16. The computer program product of claim 15, wherein the operations comprise generating, by a pattern compiler, a plurality of transformations of a stream processing platform.

17. The computer program product of claim 15, wherein the output data element specifies an occurrence of a given event based on the determination that the one or more co-occurrence criteria of the stream matching pattern are satisfied.

18. The computer program product of claim 17, wherein the operations comprise determining a probability that the given event occurred and including, in the output data element, data specifying the probability.

19. The computer program product of claim 15, wherein determining whether the one or more co-occurrence criteria of the stream matching pattern are satisfied comprises:

detecting, based on a first stream of events received from a first real-world sensor, an occurrence of a first constituent event defined by the user-specified stream matching pattern within the time window;

detecting, based on a second stream of events received from a second real-world sensor different from the first real-world sensor, an occurrence of a second constituent event defined by the user-specified stream matching pattern within the time window; and

determining that the one or more co-occurrence criteria of the stream matching pattern are satisfied based at least on the first constituent event and the second constituent event occurring within the time window.

20. The computer program product of claim 19, wherein:

the stream matching pattern specifies a threshold value for the first constituent event; and

the first constituent event is detected in response to detecting one or more events in the first stream of events that satisfies the threshold value.