Data integration techniques for use in enterprise architecture modeling

• US 7,424,702 B1
• Filed: 11/01/2002
• Issued: 09/09/2008
• Est. Priority Date: 08/19/2002
• Status: Active Grant

• Current Assignee: T-Mobile Innovations LLC (Deutsche Telekom AG)
• Original Assignee: Sprint Communications Company LP (Deutsche Telekom AG)
• Inventors: Gifford, Thomas C., Vinodkrishnan, Nalledath Palat
• Primary Examiner(s): INGBERG, TODD D
• Application Number:
  US10/285,884
• Time in Patent Office: 2,139 Days
• Field of Search: 717124-127, 719/313, 719/311, 719/330
• US Class Current: 717/124
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/063114 Status monitoring or status determination for a person or group
• CPC Class Codes: G06Q 10/06313 Resource planning in a project environment

Abstract

A device for modeling an integrated enterprise includes a first tool for constructing a model of the integrated enterprise and a second tool for analyzing calls, contained in the constructed model, between applications of the integrated enterprise and a database shared thereby. The call analysis tool identifies data attribute imbalances in calls between the applications of the integrated enterprise and the shared database. Data attribute imbalances result if the calls attempt to consume data which was never produced or if the calls produce data which is never consumed. The call analysis tool also generates producer/consumer maps of the data attributes used in the model, producer exception reports which identify data attributes which are consumed but never produced and consumer exception reports which identify data attributes which are produced but never consumed.

Claims

1. A method of modeling an enterprise having a shared database, comprising:

constructing an interim model of said enterprise;

analyzing modeled producers and modeled consumers of modeled data within said interim model of said enterprise;

identifying modeled data which is consumed in the model but not produced in the model based on the analyzing;

identifying modeled data which is produced in the model but not consumed in the model based on the analyzing; and

producing an integrated model of said enterprise using said analysis of modeled producers and modeled consumers of modeled data within said interim model of said enterprise to integrate said shared database into said interim model of said enterprise.

2. The method of claim 1, wherein analyzing modeled producers and modeled consumers of modeled data within said interim model of said enterprise further comprises analyzing modeled calls to said shared database on a data attribute-by-data attribute basis.

3. The method of claim 1, and further comprising integrating said shared database into said interim model of said enterprise to produce said integrated model of said enterprise by revising said interim model of said enterprise such that said integrated model of said enterprise produces said data identified as being consumed in the model but not produced in the model and consumes said data identified as being produced in the model but not consumed in the model.

4. A method of constructing an integrated model of an enterprise having a shared database, comprising:

constructing an interim model of said enterprise, said interim model comprised of a set of sequence diagrams;

selecting a scenario of said interim model of said enterprise for further analysis, said scenario comprised of a subset of said set of sequence diagrams;

generating, from said scenario, a map of data producers and data consumers, said map identifying at least one data attribute contained in said scenario, each producer of each of said at least one identified data attribute and each consumer of each of said at least one data attribute;

generating, from said scenario, an exception for each one of said at least one data attribute produced but not consumed or consumed but not produced;

producing said integrated model of said enterprise by integrating said interim model of said enterprise from said generated map of data producers and data consumers and said generated exception for each one of said at least one data attribute produced but not consumed or consumed but not produced.

5. The method of claim 4, wherein integrating said interim model of said enterprise from said generated map of data producers and data consumers and said generated exception for each one of said at least one data attribute produced but not consumed or consumed but not produced further comprises:

modifying usage of each one of said at least one data attribute contained in said scenario and identified by one of said at least one generated exception as being produced but not consumed or consumed but not produced.

6. For an enterprise having at least two applications and a database shared between said at least two applications, a method of integrating a model of said enterprise, comprising:

analyzing a plurality of modeled calls between said at least two modeled applications and said modeled shared database, said plurality of modeled calls including calls of at least two call types;

determining whether said analyzed plurality of modeled calls contains a modeled data imbalance, said modeled data imbalance occurring when said analyzed plurality of modeled calls includes at least one modeled data attribute contained in at least one of said analyzed modeled calls of a first one of said at least two call types but not contained in any of said analyzed plurality of modeled calls of a second one of said at least two call types; and

revising said model of said enterprise to remove said modeled data imbalance when said analyzed plurality of modeled calls contains the modeled data imbalance.

7. The method of claim 6, wherein said first one of said at least two call types is data producing modeled calls.

8. The method of claim 6, wherein said first one of said at least two call types is data producing modeled calls and said second one of said at least two call types is data consuming modeled calls.

9. The method of claim 6, wherein said first one of said at least two call types is data consuming modeled calls.

10. The method of claim 6, and further comprising generating an exception report listing said modeled data attributes identified as being contained in at least one of said analyzed plurality of modeled calls of said first type but not contained in any of said analyzed plurality of modeled calls of said second type.

11. The method of claim 10, wherein said first one of said at least two calls types is data producing modeled calls and said exception report listing said modeled data attributes identified as being contained in at least one of said analyzed plurality of modeled calls of said first type but not contained in any of said analyzed plurality of modeled calls of said second type is a consumer exception report.

12. The method of claim 10, wherein said first one of said at least two call types is data consuming modeled calls and said exception report listing said modeled data attributes identified as being contained in at least one of said analyzed plurality of modeled calls of said first type but not contained in any of said analyzed plurality of modeled calls of said second type is a producer exception report.

13. The method of claim 6, and further comprising identifying at least one modeled data attribute contained in at least one of said analyzed plurality of modeled calls of said second one of said at least two call types but not contained in any of said analyzed plurality of modeled calls of said first one of said at least two call types.

14. The method of claim 13, and further comprising:

generating a first exception report listing said modeled data attributes identified as being contained in at least one of said analyzed plurality of modeled calls of said first type but not contained in any of said analyzed plurality of modeled calls of said second type; and

generating a second exception report listing said modeled data attributes identified as being contained in at least one of said analyzed plurality of modeled calls of said second type but not contained in any of said analyzed plurality of modeled calls of said first type.

15. The method of claim 14, wherein said first one of said at least two calls types is data producing modeled calls, said first exception report listing said modeled data attributes identified as being contained in at least one of said analyzed plurality of modeled calls of said first type but not contained in any of said analyzed plurality of modeled calls of said second type is a consumer exception report, said second one of said at least two call types is data consuming modeled calls and said second exception report listing said modeled data attributes identified as being contained in at least one of said analyzed plurality of modeled calls of said second type but not contained in any of said analyzed plurality of modeled calls of said first type is a producer exception report.

16. The method of claim 6, wherein revising said model of said enterprise to remove said modeled data imbalance in said analyzed plurality of modeled calls further comprises revising said model of said enterprise so that each one of said identified modeled data attributes are contained in at least one modeled call of said first call type and at least one modeled call of said second call type.

17. The method of claim 16, wherein the step of revising said model of said enterprise so that each one of said identified modeled data attributes is contained in at least one modeled call of said first call type and at least one modeled call of said second call type further comprises:

adding, to said model, at least one modeled call of said second call type;

wherein said at least one identified modeled data attribute is contained in said at least one added modeled call.

18. The method of claim 16, wherein the step of revising said model of said enterprise so that each one of said identified modeled data attributes is contained in at least one modeled call of said first call type and at least one modeled call of said second call type further comprises:

revising at least one modeled call of said second call type;

wherein said at least one revised modeled call contains said at least one identified modeled data attribute.

19. The method of claim 6, wherein revising said model of said enterprise to remove said modeled data imbalance in said analyzed plurality of modeled calls further comprises revising said model of said integrated enterprise so that each one of said identified modeled data attributes is no longer contained in any of said at least one modeled call of said first call type.

20. The method of claim 19, wherein the step of revising said model of said enterprise so that each one of said identified modeled data attributes is no longer contained in any of said at least one modeled call of said first call type further comprises deleting, from said model, at least one modeled call of said first call type.

21. The method of claim 20, wherein the step of revising said model of said enterprise so that each one of said identified modeled data attributes is no longer contained in any of said at least one modeled call of said first call type further comprises:

revising at least one modeled call of said first call type;

wherein none of said at least one revised modeled call contains any of said at least one identified modeled data attribute.

22. A method of integrating a hierarchical model of an enterprise, comprising:

inverting said hierarchical model of said enterprise to form an inverted hierarchical model of said enterprise, wherein said inverted hierarchical model of said enterprise includes an inverted hierarchical model first level identifying at least one data attribute, each one of said at least one data attribute associated with one or more operations and an inverted hierarchical model second level identifying each one of said one or more operations associated with each one of said at least one data attribute; and

analyzing said inverted hierarchical model of said enterprise;

classifying each one of said at least one operation as either a data producing type operation or as a data consuming type operation;

constructing a producer exception report based on the analyzing and classifying, the producer exception report listing each one of said at least one data attribute associated with at least one data consuming operation but not associated with any data producing operations;

constructing a consumer exception report based on said analyzing and said classifying, the consumer exception report listing each one of said at least one data attribute associated with at least one data producing operation but not associated with any data consuming operation; and

modifying said hierarchical model of said enterprise based at least on said producer exception report and said consumer exception report.

23. The method of claim 22, wherein said hierarchical model of said enterprise includes a hierarchical model first level identifying at least one task to be performed by said enterprise, a hierarchical model second level identifying, for each one of said at least one task, at least one operation forming part of said task, and a hierarchical model third level identifying, for each one of said at least one operation forming part of one of said at least one task, at least one data attribute associated with said operation and wherein said second level of said inverted hierarchical model of said enterprise further identifies, for each one of said one or more operations associated with each one of said at least one data attribute, said task for which said operation was conducted in connection therewith.

24. The method of claim 22, wherein modifying said hierarchical model of said enterprise comprises modifying said hierarchical model such that each data attribute listed in said producer exception report is associated with at least one data producing operation.

25. The method of claim 22, wherein modifying said hierarchical model of said enterprise comprises modifying said hierarchical model such that each data attribute listed in said consumer exception report is associated with at least one data consuming operation.