Structuring data

• US 9,613,112 B2
• Filed: 03/15/2013
• Issued: 04/04/2017
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: MIOsoft Corporation
• Original Assignee: MIOsoft Corporation
• Inventors: Wimmer, Helmut, Siepmann, Ernst M., Barabas, Albert B., van Gulik, Mark D. A., Baamann, Katharina
• Primary Examiner(s): Stevens, Robert
• Application Number:
  US13/841,730
• Publication Number:
  US 20140279942A1
• Time in Patent Office: 1,481 Days
• Field of Search: 707690, 707803
• CPC Class Codes: G06F 16/21 Design, administration or maintenance of databases
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 16/252 between a Database Management System and a front-end application
• CPC Class Codes: G06F 16/285 Clustering or classification

Abstract

Among other things, a machine-based method is described. The method comprises recording object classes of an object model, producing an object representation for data of two or more data sources based on a mapping of data formats of the data sources to the object classes of the object model, and producing mapped data from the data sources. The mapped data is available in objects of the object classes and is comparable in the object representation. At least two of the data sources have different data formats.

Claims

1. A machine-based method comprising:

reducing usage of memory in a computer by reducing a volume of data that needs to be held in the memory to perform a data analysis associated with a context that affects the identity of the items of data to be held in the memory,

the items of data being drawn from larger volumes of items of data of two or more data sources that are at least partially stored other than in the memory, the reducing of the volume of items of data comprising

selecting, a set of object classes having associations with a particular process within an object model in accordance with the data analysis to be performed on data of the two or more data sources,

excluding from the set of object classes at least one object class not having the associations,

the associations of the object classes with the particular process being based on relationships between the object classes, the relationships being identified based on relevances of the relationships to the context associated with the data analysis to be performed;

producing an object representation of the items of data of the two or more data sources to be held in the memory based on a mapping of data formats of the data sources to the object classes, at least two of the data sources having different data formats, the producing of the object representation including;

(A) mapping first data of a first data source of the two or more data sources to instances of objects of the selected object classes; and

(B) mapping second data of a second data source of the two or more data sources to instances of objects of the selected object classes,

producing mapped data from the data sources, the mapped data being available in the instances of the objects of the object classes and comparable as between the data sources in the object representation based on mapping (A) and mapping (B), and

holding the mapped data as the reduced volume of data in the memory while the data analysis is performed.

2. The machine-based method of claim 1, comprising storing the object representation.

3. The machine-based method of claim 2, wherein storing the object representation comprises storing meta data of the object representation.

4. The machine-based method of claim 1, comprising storing the objects in a database.

5. The machine-based method of claim 1, comprising grouping at least some of the object classes into groups based on a real-world business context of interest in a real-world business entity.

6. The machine-based method of claim 5, comprising associating the mapped data with instances of the groups to which the object classes belong.

7. The machine-based method of claim 6, wherein at least one of the data sources comprises a stream of continuous data, and the method comprises continuously producing mapped data in the object representation and continuously associating the mapped data with the instances of the groups.

8. The machine-based method of claim 6, comprising executing an algorithm defined on a group on an instance of the group without needing data outside of the instance.

9. The machine-based method of claim 6, wherein a relationship is defined between object classes in different groups and the method comprises maintaining the consistency of the relationship among the mapped data of the instances of the different groups.

10. The machine-based method of claim 9, wherein maintaining the consistency comprises executing an algorithm in one instance and updating the data in another, different instance based on the execution.

11. The machine-based method of claim 6, wherein the mapped data of an instance comprises fragments each containing data mapped from a single data source in the object representation, and the method comprises associating different fragments to the instance.

12. The machine-based method of claim 11, comprising applying one or more rules to the different fragments to determine whether the fragments are associated with the same instance.

13. The machine-based method of claim 12, wherein the one or more rules comprise a match algorithm.

14. The machine-based method of claim 12, wherein the different fragments are associated with the same instance when any one of the one or more rules is satisfied.

15. The machine-based method of claim 14, wherein a rule is satisfied when all attributes of the rule are satisfied.

16. The machine-based method of claim 6, comprising dividing an instance of a group into two or more sub-instances, each sub-instance containing at least one object that is not contained in another sub-instance.

17. The machine-based method of claim 1, wherein the data of the two or more data sources comprises real business data contained in a snapshot taken at a time point of a real-world business process of a real-world business entity.

18. The machine-based method of claim 1, wherein the data of the two or more data sources comprises real business data contained in a data capture performed for a predetermined period of time during a real-world process of a real-world entity.

19. The machine-based method of claim 1, wherein at least one of the data sources comprises a stream of continuous data, and the method comprises continuously producing mapped data in the object representation.

20. The machine-based method of claim 1, comprising executing an algorithm that involves the data from the two or more data sources based on the object representation of the business data.

21. The machine-based method of claim 20, wherein the algorithm comprises comparing real business data of real-world business processes from the different data sources.

22. The machine-based method of claim 20, wherein the algorithm comprises computing customer satisfaction or analyzing customer behavior.

23. The machine-based method of claim 20, wherein the algorithm comprises identifying a business trend.

24. The machine-based method of claim 20, wherein the algorithm facilitates risk management and compliance.

25. The machine-based method of claim 1, comprising executing an algorithm that involves business data from at least one of the two or more data sources based on the object representation of the data and wherein the algorithm comprises evaluating quality of real business data from real-world business processes of a real-world business entity and understanding the real-world business processes in the real-world business entity, and the method comprises:

grouping the real business data into instances of groups based on a business context of interest to be analyzed for the real-world business entity,

comparing the real business data with expected business data to identify possible issues with the real business data, and

searching for a root cause of the identified issues based on the grouping.

26. The machine-based method of claim 25, wherein searching for the root cause comprises examining real-world systems within the real-world business entity and interfaces between the real-world systems from which the real business data having the identified issues originates to locate the origin of the identified issues.

27. The machine-based method of claim 25, comprising categorizing the instances based on the identified issues.

28. The machine-based method of claim 1, comprising enabling a user to provide input for defining the object classes.

29. The machine-based method of claim 1, comprising enabling a user to provide specifications for mapping the data formats.

30. A computer program product residing on a non-transitory computer readable medium, the computer program product comprising instructions for causing a processor to:

reduce usage of memory in a computer by reducing a volume of data that needs to be held in the memory to perform a data analysis associated with a context that affects the identity of the items of data to be held in the memory,

the items of data being drawn from larger volumes of items of data of two or more data sources that are at least partially stored other than in the memory,

in order to reduce the volume of items of data, among other things;

select a set of object classes having associations with a particular process within an object model in accordance with the data analysis to be performed on data of two or more data sources;

produce an object representation of the items of data of the two or more data sources to be held in the memory based on a mapping of data formats of the data sources to the object classes, at least two of the data sources having different data formats, the producing of the object representation including;

(A) mapping first data of a first data source of the two or more data sources to instances of objects of the selected object classes; and

(B) mapping second data of a second data source of the two or more data sources to instances of objects of the selected object classes,

produce mapped data from the data sources, the mapped data being available in the instances of the objects of the object classes and comparable as between the data sources in the object representation based on mapping (A) and mapping (B), and

hold the mapped data as the reduced volume of data in the memory while the data analysis is performed.

31. The computer program product of claim 30, comprising instructions for causing the processor to store the object representation.

32. The computer program product of claim 31, wherein storing the object representation comprises storing meta data of the object representation.

33. The computer program product of claim 30, comprising instructions for causing the processor to store the objects in a database.

34. The computer program product of claim 30, comprising instructions for causing the processor to group at least some of the object classes into groups based on a real-world business context of interest in a real-world business entity.

35. The computer program product of claim 34, comprising instructions for causing the processor to associate the mapped data with instances of the groups to which the object classes belong.

36. The computer program product of claim 35, wherein at least one of the data sources comprises a stream of continuous business data, and wherein the program comprises instructions for causing the processor to continuously produce mapped data in the object representation and continuously associate the mapped data with the instances of the groups.

37. The computer program product of claim 35, comprising instructions for causing the processor to execute an algorithm defined on a group on an instance of the group without needing data outside of the instance.

38. The computer program product of claim 35, wherein a relationship is defined between object classes in different groups and the program comprises instructions causing the processor to maintain the consistency of the relationship among the mapped data of the instances of the different groups.

39. The computer program product of claim 35, wherein maintaining the consistency comprises executing an algorithm in one instance and updating the data in another, different instance based on the execution.

40. The computer program product of claim 35, wherein the mapped data of an instance comprises fragments each containing data mapped from a single data source in the object representation, and the program comprises instructions causing the processor to associate different fragments to the instance.

41. The computer program product of claim 40, comprising instructions causing the processor to apply one or more rules to the different fragments to determine whether the fragments are associated with the same instance.

42. The computer program product of claim 41, wherein the one or more rules comprises a match algorithm.

43. The computer program product of claim 41, wherein the different fragments are associated with the same instance when any one of the one or more rules is satisfied.

44. The computer program product of claim 43, wherein a rule is satisfied when all attributes of the rule is satisfied.

45. The computer program product of claim 30, wherein the data of the two or more data sources comprises real data contained in a snapshot taken at a time point of a real-world process of a real-world entity.

46. The computer program product of claim 30, wherein the data of the two or more data sources comprises real business data contained in a data capture performed for a predetermined period of time during a real-world business process of a real-world business entity.

47. The computer program product of claim 30, wherein at least one of the data sources comprises a stream of continuous data, and the program comprises instructions for the processor to continuously produce mapped data in the object representation.

48. The computer program product of claim 30, comprising instructions causing the processor to execute an algorithm that involves the data from the two or more data sources based on the object representation of the data.

49. The computer program product of claim 48, wherein the algorithm comprises comparing real business data of real-world business processes from the different data sources.

50. The computer program product of claim 48, wherein the algorithm comprises computing customer satisfaction or analyzing customer behavior.

51. The computer program product of claim 48, wherein the algorithm comprises identifying a business trend.

52. The computer program product of claim 48, wherein the algorithm facilitates risk management and compliance.

53. The computer program product of claim 30, comprising instructions causing the processor to execute an algorithm that involves data from at least one of the two or more data sources based on the object representation of the data and wherein the algorithm comprises evaluating quality of real business data from real-world business processes of a real-world business entity and understanding the real-world business processes in the real-world business entity, and the program comprises instructions causing the processor to:

group the real data into instances of groups based on a context of interest to be analyzed for the real-world business entity,

compare the real data with expected data to identify possible issues with the real data, and

search for a root cause of the identified issues based on the grouping.

54. The computer program product of claim 53, wherein searching for the root cause comprises examining real-world systems within the real-world business entity and interfaces between the real-world systems from which the real data having the identified issues originates to locate the origin of the identified issues.

55. The computer program product of claim 53, comprising instructions causing the processor to categorize the instances based on the identified issues.

56. The computer program product of claim 30, comprising instructions causing the processor to enable a user to provide input for defining the object classes.

57. The computer program product of claim 30, comprising instructions causing the processor to enable a user to provide specifications for mapping the data formats.

58. The computer program product of claim 30, comprising instructions for causing the processor to divide an instance of a group into two or more sub-instances, each sub-instance containing at least one object that is not contained in another sub-instance.

59. A computer system comprising:

a processor;

a memory; and

a storage device that stores a program for execution by the processor using the memory, the program comprising instructions configured to cause the processor to;

reduce usage of the memory by reducing a volume of data that needs to be held in the memory to perform a data analysis associated with a context that affects the identity of the items of data to be held in the memory,

the items of data being drawn from larger volumes of items of data of two or more data sources that are at least partially stored other than in the memory,

in order to reduce the volume of items of data, among other things;

select a set of object classes having associations with a particular process within an object model in accordance with the data analysis to be performed on data of two or more data sources;

produce an object representation of the items of data of the two or more data sources to be held in the memory based on a mapping of data formats of the data sources to the object classes, at least two of the data sources having different data formats, the producing of the object representation including;

(A) mapping first data of a first data source of the two or more data sources to instances of objects of the selected object classes; and

(B) mapping second data of a second data source of the two or more data sources to instances of objects of the selected object classes,

produce mapped data from the data sources, the mapped data being available in the instances of the objects of the object classes and comparable as between the data sources in the object representation based on mapping (A) and mapping (B), and

hold the mapped data as the reduced volume of data in the memory while the data analysis is performed.

60. The system of claim 59, wherein the program comprises instructions configured to cause the processor to store the object representation.

61. The system of claim 60, wherein storing the object representation comprises storing meta data of the object representation.

62. The system of claim 59, wherein the program comprises instructions configured to cause the processor to store the objects in a database.

63. The system of claim 59, wherein the program comprises instructions configured to cause the processor to group at least some of the object classes into groups based on a real-world context of interest in a real-world business entity.

64. The system of claim 63, wherein the program comprises instructions configured to cause the processor to associate the mapped data with instances of the groups to which the object classes belong.

65. The system of claim 64, wherein at least one of the data sources comprises a stream of continuous data, and wherein the program comprises instructions configured to cause the processor to continuously produce mapped data in the object representation and continuously associate the mapped data with the instances of the groups.

66. The system of claim 64, wherein the program comprises instructions configured to cause the processor to execute an algorithm defined on a group on an instance of the group without needing data outside of the instance.

67. The system of claim 64, wherein a relationship is defined between object classes in different groups and the program comprises instructions configured to cause the processor to maintain the consistency of the relationship among the mapped data of the instances of the different groups.

68. The system of claim 67, wherein maintaining the consistency comprises executing an algorithm in one instance and updating the data in another, different instance based on the execution.

69. The system of claim 64, wherein the mapped data of an instance comprises fragments each containing data mapped from a single data source in the object representation, and wherein the program comprises instructions configured to cause the processor to associate different fragments to the instance.

70. The system of claim 69, wherein the program comprises instructions configured to cause the processor to apply one or more rules to the different fragments to determine whether the fragments are associated with the same instance.

71. The system of claim 70, wherein the one or more rules comprises a match algorithm.

72. The system of claim 70, wherein the different fragments are associated with the same instance when any one of the one or more rules is satisfied.

73. The system of claim 72, wherein a rule is satisfied when all attributes of the rule are satisfied.

74. The system of claim 59, wherein the data of the two or more data sources comprises real data contained in a data capture performed for a predetermined period of time during the real-world process of the real-world entity.

75. The system of claim 59, wherein the data of the two or more data sources comprises real data contained in a data capture performed for a predetermined period of time during the real-world process of the real-world entity.

76. The system of claim 59, wherein at least one of the data sources comprises a stream of continuous data, and wherein the program comprises instructions configured to cause the processor to continuously produce mapped data in the object representation.

77. The system of claim 59, wherein the program comprises instructions configured to cause the processor to execute an algorithm that involves the data from the two or more data sources based on the object representation of the data.

78. The system of claim 77, wherein the algorithm comprises comparing real business data of real-world business processes from the different data sources.

79. The system of claim 77, wherein the algorithm comprises computing customer satisfaction or analyzing customer behavior.

80. The system of claim 77, wherein the algorithm comprises identifying a business trend.

81. The system of claim 77, wherein the algorithm facilitates risk management and compliance.

82. The system of claim 59, wherein the program comprises instructions configured to cause the processor to execute an algorithm that involves data from at least one of the two or more data sources based on the object representation of the data and wherein the algorithm comprises evaluating quality of real data from real-world business processes of a real-world business entity and understanding the business real-world processes in the real-world business entity, and wherein the program comprises instructions configured to cause the processor to:

group the real data into instances of groups based on a context of interest to be analyzed for the real-world business entity,

compare the real data with expected data to identify possible issues with the real data, and

search for a root cause of the identified issues based on the grouping.

83. The system of claim 82, wherein searching for the root cause comprises examining real-world systems within the real-world entity and interfaces between the real-world systems from which the real data having the identified issues originates to locate the origin of the identified issues.

84. The system of claim 82, wherein the program comprises instructions configured to cause the processor to categorize the instances based on the identified issues.

85. The system of claim 59, wherein the program comprises instructions configured to cause the processor to enable a user to provide input for defining the object classes.

86. The system of claim 59, wherein the program comprises instructions configured to cause the processor to enable a user to provide specification for mapping the data formats.

87. The system of claim 59, wherein the program comprises instructions configured to cause the processor to divide an instance of a group into two or more sub-instances, each sub-instance containing at least one object that is not contained in another sub-instance.