Platform data aggregation and semantic modeling

• US 10,599,679 B2
• Filed: 04/07/2016
• Issued: 03/24/2020
• Est. Priority Date: 08/17/2015
• Status: Active Grant

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: Tiell, Steven C., Liongosari, Edy S., Kundavaram, Chetan R., Modi, Shimon
• Primary Examiner(s): Mofiz, Apu M
• Assistant Examiner(s): Nguyen, Cindy
• Application Number:
  US15/093,344
• Publication Number:
  US 20170053015A1
• Time in Patent Office: 1,447 Days
• CPC Class Codes: G06F 16/113 Details of archiving lifecycle management in storage systems G06F3/0649; backup systems G06F11/1446
• CPC Class Codes: G06F 16/162 Delete operations erasing in storage systems G06F3/0652
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 21/6227 where protection concerns the structure of data, e.g. records, types, queries
• CPC Class Codes: G06F 21/6236 between heterogeneous systems
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Techniques are described for aggregating data generated by multiple platforms of different types. A particular user (e.g., end-user) may interact with multiple individual (e.g., siloed) platforms of different types and/or that support different business purposes or industries. The individual platforms may generate data describing and/or resulting from these interactions. The data may be received, ingested, and processed by a super-platform. The super-platform may generate aggregate data by aggregating the data received from different individual platforms. Data aggregation may be performed on data that is generated by different individual platforms and that is associated with a particular user or multiple users. Aggregation may also be performed on data that is independent of any particular user, such as sensor data that describes an environment in proximity to the platform.

Claims

1. A computer-implemented method performed by at least one processor, the method comprising:

receiving, by a super-platform executed by the at least one processor, data objects generated by a plurality of platforms that provide at least two different types of services, the super-platform receiving data objects from each of a plurality of platforms;

providing, by the super-platform, for each respective platform, a platform model comprising a platform identifier that uniquely identifies the respective platform, data access information that describes how to access the data objects generated by the respective platform, and a semantic data model that describes the data objects generated by the respective platform, wherein the semantic data model comprises a directed graph that describes relationships between pairs of the data objects generated by the respective platform;

adding, by the super-platform, at least one platform through dynamically adapting to the at least one platform by determining how a respective instance of data provided by the at least one platform fits into a semantic data model that describes previously ingested data from another platform;

aggregating, by the super-platform, at least two of the data objects from at least two of the plurality of platforms to generate one or more aggregate data objects, wherein the aggregating is based on;

i) metadata that describes the at least two data objects, and ii) respective semantic data models that each describe the data objects generated by respective platforms of the at least two platforms, and wherein the aggregating of the at least two data objects is based at least partly on one or more relationships that include the at least two data objects and that are described in the respective directed graph for each of the at least two platforms; and

providing, by the super-platform, the one or more aggregate data objects to at least one data consumer device.

2. The method of claim 1, wherein one or more of the data objects include data associated with an end-user of one or more of the plurality of platforms.

3. The method of claim 1, further comprising:

determining, by the at least one processor, that a particular data object from one of the plurality of platforms includes data not included in other data objects from other platforms; and

including, by the at least one processor, the particular data object in the at least some of the data objects that are aggregated to generate the one or more aggregate data objects.

4. The method of claim 1, further comprising:

normalizing, by the at least one processor, the data from different platforms of the plurality of platforms to provide one or more of a common data format, a common data style, or a common data type for the data.

5. The method of claim 4, wherein:

at least two platforms of the plurality of platforms generate data associated with a same end-user;

the at least two platforms employ at least two different user identifiers to identify the same end-user; and

normalizing the data further comprises associating a common user identifier with the data generated by the at least two platforms.

6. The method of claim 1, wherein at least a portion of the metadata is received from one or more of the plurality of platforms.

7. The method of claim 1, further comprising:

generating, by the at least one processor, at least a portion of the metadata based on one or more of;

a natural language analysis of the data;

a pattern matching analysis of the data;

or

context information associated with one or more of the plurality of platforms or an end-user of the plurality of platforms.

8. The method of claim 1, wherein the plurality of platforms include at least two of:

an e-commerce platform configured to provide an e-commerce service;

a retail platform configured to support retail operations of a physical store;

a media platform configured to provide an online media service;

a mobile computing device;

an Internet of Things (IoT) computing device;

or

an in-vehicle computer.

9. The method of claim 1, further comprising:

analyzing, by the at least one processor, data included in one or more of the data objects to determine a semantic data model for one of the plurality of platforms; and

employing, by the at least one processor, the semantic data model in aggregating at least some of the data objects from the plurality of platforms to generate the one or more aggregate data objects.

10. The method of claim 9, further comprising:

receiving, by the at least one processor, an indication of a newly available platform configured to provide data; and

based at least partly on a similarity between the newly available platform and one of the plurality of platforms, determining, by the at least one processor, a semantic data model for the newly available platform.

11. A system comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising;

receiving, by a super-platform, data objects generated by a plurality of platforms that provide at least two different types of services, the super-platform receiving data objects from each of a plurality of platforms;

providing, by the super-platform, for each respective platform, a platform model comprising a platform identifier that uniquely identifies the respective platform, data access information that describes how to access the data objects generated by the respective platform, and a semantic data model that describes the data objects generated by the respective platform, wherein the semantic data model comprises a directed graph that describes relationships between pairs of the data objects generated by the respective platform;

adding, by the super-platform, at least one platform through dynamically adapting to the at least one platform by determining how a respective instance of data provided by the at least one platform fits into a semantic data model that describes previously ingested data from another platform;

aggregating, by the super-platform, at least two of the data objects from at least two of the plurality of platforms to generate one or more aggregate data objects, wherein the aggregating is based on;

i) metadata that describes the at least two data objects, and ii) respective semantic data models that each describe the data objects generated by respective platforms of the at least two platforms, and wherein the aggregating of the at least two data objects is based at least partly on one or more relationships that include the at least two data objects and that are described in the respective directed graph for each of the at least two platforms; and

providing, by the super-platform, the one or more aggregate data objects to at least one data consumer device.

12. The system of claim 11, wherein one or more of the data objects include data associated with an end-user of one or more of the plurality of platforms.

13. The system of claim 11, the operations further comprising:

determining that a particular data object from one of the plurality of platforms includes data not included in other data objects from other platforms; and

including the particular data object in the at least some of the data objects that are aggregated to generate the one or more aggregate data objects.

14. The system of claim 11, the operations further comprising:

normalizing the data from different platforms of the plurality of platforms to provide one or more of a common data format, a common data style, or a common data type for the data.

15. The system of claim 14, wherein:

at least two platforms of the plurality of platforms generate data associated with a same end-user;

the at least two platforms employ at least two different user identifiers to identify the same end-user; and

normalizing the data further comprises associating a common user identifier with the data generated by the at least two platforms.

16. One or more non-transitory computer-readable storage media storing instructions which, when executed by at least one processor, cause the at least one processor to perform operations comprising:

receiving, by a super-platform, data objects generated by a plurality of platforms that provide at least two different types of services, the super-platform receiving data objects from each of a plurality of platforms;

providing, by the super-platform, for each respective platform, a platform model comprising a platform identifier that uniquely identifies the respective platform, data access information that describes how to access the data objects generated by the respective platform, and a semantic data model that describes the data objects generated by the respective platform, wherein the semantic data model comprises a directed graph that describes relationships between pairs of the data objects generated by the respective platform;

adding, by the super-platform, at least one platform through dynamically adapting to the at least one platform by determining how a respective instance of data provided by the at least one platform fits into a semantic data model that describes previously ingested data from another platform;

aggregating, by the super-platform, at least two of the data objects from at least two of the plurality of platforms to generate one or more aggregate data objects, wherein the aggregating is based on;

i) metadata that describes the at least two data objects, and ii) respective semantic data models that each describe the data objects generated by respective platforms of the at least two platforms, and wherein the aggregating of the at least two data objects is based at least partly on one or more relationships that include the at least two data objects and that are described in the respective directed graph for each of the at least two platforms; and

providing, by the super-platform, the one or more aggregate data objects to at least one data consumer device.

17. The one or more non-transitory computer-readable storage media of claim 16, wherein at least a portion of the metadata is received from one or more of the plurality of platforms.

18. The one or more non-transitory computer-readable storage media of claim 16, the operations further comprising:

generating at least a portion of the metadata based on one or more of;

a natural language analysis of the data;

a pattern matching analysis of the data;

or

context information associated with one or more of the plurality of platforms or an end-user of the plurality of platforms.

19. The one or more non-transitory computer-readable storage media of claim 16, wherein the plurality of platforms include at least two of:

an e-commerce platform configured to provide an e-commerce service;

a retail platform configured to support retail operations of a physical store;

a media platform configured to provide an online media service;

a mobile computing device;

an Internet of Things (IoT) computing device;

or

an in-vehicle computer.

20. The one or more non-transitory computer-readable storage media of claim 16, the operations further comprising:

analyzing data included in one or more of the data objects to determine a semantic data model for one of the plurality of platforms; and

employing the semantic data model in aggregating at least some of the data objects from the plurality of platforms to generate the one or more aggregate data objects.