METHOD FOR ANALYZING TIME SERIES ACTIVITY STREAMS AND DEVICES THEREOF

• US 20150213152A1
• Filed: 12/08/2014
• Published: 07/30/2015
• Est. Priority Date: 11/16/2011
• Status: Active Grant

• Current Assignee: PTC Inc.
• Original Assignee: PTC Inc.
• Inventors: Bullotta, Rick, Schaefer, John
• Granted Patent: US 9,348,943 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2477 Temporal data queries
• CPC Class Codes: G06F 16/27 Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
• CPC Class Codes: G06F 16/903 Querying for retrieval from the web G06F16/953
• CPC Class Codes: G06F 16/9038 Presentation of query results
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: H04L 43/08 Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters

Abstract

A method, non-transitory computer readable medium, and data manager computing device comprises retrieving a time series data of a monitored asset based on one or more tags in response to a request. Further, a heterogeneous data based on the one or more tags is retrieved. Furthermore, a cause of an anomaly period in retrieved time series data of the monitored asset is determined based on the retrieved heterogeneous data. Finally, the determined cause of the anomaly period in the time series data in the monitored asset is provided.

Claims

1. A method for analyzing a time series activity stream, the method comprising:

retrieving by a data management computing apparatus a time series data of a monitored asset based on one or more tags in response to a request;

retrieving by the data management computing apparatus a heterogeneous data based on the one or more tags;

determining by the data management computing apparatus a cause of an anomaly period in retrieved time series data of the monitored asset based on the retrieved heterogeneous data; and

providing by the data management computing apparatus the determined cause of the anomaly period in the time series data in the monitored asset.

2. The method as set forth in claim 1 further comprising identifying by the data management computing apparatus the anomaly period in time series data associated with the monitored asset.

3. The method as set forth in claim 1 further comprising:

retrieving by the data management computing apparatus a heterogeneous contextual data based on a stored model relationship related to a monitored asset during an anomaly period of a time series data;

retrieving by the data management computing apparatus a related heterogeneous asset data based on the stored model relationship;

determining by the data management computing apparatus the cause of the anomaly period in the monitored asset based on the retrieved time series data, the retrieved heterogeneous data, the retrieved heterogeneous contextual data and the retrieved heterogeneous related heterogeneous asset data.

4. The method as set forth in claim 3,

wherein the heterogeneous contextual data comprises one or more of an environmental data from one or more environmental sensors, a business process information from one or more applications on one or more third party computing devices, and a human entered data from one or more blogs or social media systems;

and

wherein the related heterogeneous asset data further comprises at least one of an upstream heterogeneous data from an upstream source or a downstream heterogeneous data from a downstream source.

5. The method as set forth in claim 1 further comprising:

receiving by the data management computing apparatus the time series data for the monitored asset from one or more sensors; and

embedding by the data management computing apparatus one or more tags in the time series data for the monitored asset based on one or more stored rules.

6. The method as set forth in claim 1 further comprising identifying and providing by the data management computing apparatus one or more corrective steps for the determined cause of the anomaly period.

7. The method as set forth in claim 1 wherein the heterogeneous data comprises one or more of an unstructured data from one or more unstructured data sources, a structured data from one or more structured data sources, and a crawled data from one or more third party application and index data sources.

8. A non-transitory computer readable medium having stored thereon instructions for analyzing a time series activity stream comprising machine executable code which when executed by at least one processor, causes the processor to perform steps comprising:

retrieving a time series data of a monitored asset based on one or more tags in response to a request;

retrieving a heterogeneous data based on the one or more tags;

determining a cause of an anomaly period in retrieved time series data of the monitored asset based on the retrieved heterogeneous data; and

providing the determined cause of the anomaly period in the time series data in the monitored asset.

9. The medium as set forth in claim 8 further comprising identifying the anomaly period in time series data associated with the monitored asset.

10. The medium as set forth in claim 8 further comprising:

retrieving a heterogeneous contextual data based on a stored model relationship related to a monitored asset during an anomaly period of a time series data;

retrieving a related heterogeneous asset data based on the stored model relationship;

determining the cause of the anomaly period in the monitored asset based on the retrieved time series data, the retrieved heterogeneous data, the retrieved heterogeneous contextual data and the retrieved heterogeneous related heterogeneous asset data.

11. The medium as set forth in claim 10,

wherein the heterogeneous contextual data comprises one or more of an environmental data from one or more environmental sensors, a business process information from one or more applications on one or more third party computing devices, and a human entered data from one or more blogs or social media systems;

and

wherein the related heterogeneous asset data further comprises at least one of an upstream heterogeneous data from an upstream source or a downstream heterogeneous data from a downstream source.

12. The medium as set forth in claim 8 further comprising:

receiving the time series data for the monitored asset; and

embedding one or more tags in the time series data for the monitored asset based on one or more stored rules before the retrieving the heterogeneous contextual data.

13. The medium as set forth in claim 8 further comprising identifying and providing one or more corrective steps for the determined cause of the anomaly period.

14. The medium as set forth in claim 8 wherein the heterogeneous data comprises one or more of an unstructured data from one or more unstructured data sources, a structured data from one or more structured data sources, and a crawled data from one or more third party application and index data sources.

15. A data management computing apparatus comprising:

one or more processors;

a memory coupled to the one or more processors which are configured to execute programmed instructions stored in the memory comprising;

retrieving a time series data of a monitored asset based on one or more tags in response to a request;

retrieving a heterogeneous data based on the one or more tags;

determining a cause of an anomaly period in retrieved time series data of the monitored asset based on the retrieved heterogeneous data; and

providing the determined cause of the anomaly period in the time series data in the monitored asset.

16. The apparatus as set forth in claim 15 wherein the one or more processors is further configured to execute programmed instructions stored in the memory further comprising identifying the anomaly period in time series data associated with the monitored asset.

17. The apparatus as set forth in claim 15 wherein the one or more processors is further configured to execute programmed instructions stored in the memory further comprising:

retrieving a heterogeneous contextual data based on a stored model relationship related to a monitored asset during an anomaly period of a time series data;

retrieving a related heterogeneous asset data based on the stored model relationship;

determining the cause of the anomaly period in the monitored asset based on the retrieved time series data, the retrieved heterogeneous data, the retrieved heterogeneous contextual data and the retrieved heterogeneous related heterogeneous asset data.

18. The apparatus as set forth in claim 17,

wherein the heterogeneous contextual data comprises one or more of an environmental data from one or more environmental sensors, a business process information from one or more applications on one or more third party computing devices, and a human entered data from one or more blogs or social media systems;

and

wherein the related heterogeneous asset data further comprises at least one of an upstream heterogeneous data from an upstream source or a downstream heterogeneous data from a downstream source.

19. The apparatus as set forth in claim 15 wherein the one or more processors is further configured to execute programmed instructions stored in the memory further comprising:

receiving the time series data for the monitored asset; and

embedding one or more tags in the time series data for the monitored asset based on one or more stored rules before the retrieving the heterogeneous contextual data.

20. The apparatus as set forth in claim 15 wherein the one or more processors is further configured to execute programmed instructions stored in the memory further comprising identifying and providing one or more corrective steps for the determined cause of the anomaly period.

21. The apparatus as set forth in claim 15 wherein the heterogeneous data comprises one or more of an unstructured data from one or more unstructured data sources, a structured data from one or more structured data sources, and a crawled data from one or more third party application and index data sources.