Profile engine system and method

• US 20090055828A1
• Filed: 08/22/2007
• Published: 02/26/2009
• Est. Priority Date: 08/22/2007
• Status: Abandoned Application

• Current Assignee: Actimize Liminted
• Original Assignee: Actimize Liminted
• Inventors: Wicks, Antony James, McLaren, Iain Douglas, Mangat, Anoop Singh
• Application Number:
  US11/894,933
• Publication Number:
  US 20090055828A1
• Time in Patent Office: Days
• US Class Current: 718/103
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3476 Data logging G06F11/14, G06F11/2205 take precedence
• CPC Class Codes: G06F 2201/87 Monitoring of transactions
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A system for profile record generation of input records, the system comprising: a record processor which converts the input records into a data records suitable for the profile record generation; and a statistics engine for the generation of profile records based on the data records. Furthermore, system optimization can be obtained by use of a task control method that sub-divides the aggregations of profile records into units of work that can be individually performed, the method comprising: partitioning based on a pre-determined partitioning key associated with entities to be profiled, wherein the association between the partitioning key and the entities being profiled is varied in order to optimize the profiling performance.

Claims

1. A system for profile record generation of input records, said system comprising:

a record processor which converts said input records into a data records suitable for said profile record generation; and

a statistics engine for the generation of profile records based on said data records.

2. The system according to claim 1, further comprising a task engine that prioritizes and/or processes tasks.

3. The system according to claim 1, wherein said record processor pre-sorts and subdivides groups of said input records.

4. The system according to claim 1, wherein said data records each comprise at least one data field group selected from the group consisting of:

data record feature field group, data record value field group and data record reference field group.

5. The system according to claim 4, wherein said data record feature field group comprises data fields that describe a particular feature of said data record.

6. The system according to claim 5, wherein said data fields of said data record feature field group are at least one field selected from the group consisting of:

a value representing a finite time, entity, additional characteristics associated with said input record, and other possible characteristics that may be present and transformed from said input record.

7. The system according to claim 4, wherein said data record feature field group is used by said statistics engine to identify and select features for said aggregate profile record generation.

8. The system according to claim 4, wherein said data record value field group comprises data fields that describe the values associated with said data record feature field group.

9. The system according to claim 4, wherein said statistics engine generates statistics for said data record value field group across a plurality of said data records during said profile record generation.

10. The system according to claim 4, wherein said data record reference field group comprises data fields that are copies or transformed from said input record and which are to be stored for reference purposes or for other non-profile record generation tasks.

11. The system according to claim 10, wherein said data fields of said data record reference field group are at least one selected from the group consisting of:

narrative, Field1 and Field2.

12. The system according to claim 4, wherein said profile record is produced by said statistics engine based on aggregation or other statistical processing of said data record value fields for a particular data record feature fields present in a plurality of said data records.

13. The system according to claim 12, wherein each said profile record comprises a profile record feature field group and a profile record statistics field group.

14. The system according to claim 13, wherein said profile record feature field group corresponds to a particular field present in said data record feature field group that are considered by said statistics engine during profile generation.

15. The system according to claim 13, wherein the combination of fields in said profile record feature field group defines the characteristic of the profile, wherein said combination of fields from said profile record feature field group defines which said data records are processed by said statistics engine in order to generate said profile record.

16. The system according to claim 13, wherein said statistics field group provides derived aggregate statistics for said profile record feature field group.

17. The system according to claim 13, wherein said statistics field group is created by said statistics engine through the aggregation or other mathematical manipulation of said data records identified by a particular profile record feature field group.

18. The system according to claim 13, wherein said profile record feature field group includes at least one field selected from the group consisting of:

a value representing a finite time, entity, additional characteristics associated with said data record, and other possible characteristics that may be present and transformed from said data record.

19. The system according to claim 13, wherein said profile record statistics field group includes at least one field selected from the group consisting of:

number of said data records considered in the aggregation, the maximum values located as part of the aggregation, the minimum values located as part of the aggregation, the total sum of values located as part of the aggregation, the sum of values squared.

20. The system according to claim 2, wherein said tasks are a unit of work to be performed by said system.

21. The system according to claim 20, wherein said work is creation of one or more said data records, and/or creation of one or more said profile records.

22. The system according to claim 2, wherein said task engine includes at least one task queue.

23. The system according to claim 22, wherein said task queue comprises at least one field selected from the group consisting of task field, a descriptor field, a priority field and a status field.

24. The system according to claim 23, wherein said tasks are ordered in said task queue based on the priority assigned to said task and selected for execution based on the status and an execution order assigned to said task.

25. The system according to claim 1, further comprising a field mapper which creates normalized data representations during the processing of said input records by said record processor.

26. The system according to claim 25, wherein said field mapper performs at least one transformation selected from the group consisting of:

entity substitution, reference lookup, regular expression matching, field concatenation, hash functions, phonetic encoding, format conversions, temporal substitutions, deterministic methods, substring matching and field lookup methods.

27. The system according to claim 13, further comprising a controller that creates a task for each combination of said profile record feature field group to be profiled.

28. The system according to claim 13, further comprising a controller that creates a single task to profile all of said profile record feature field groups to be profiled.

29. The system according to claim 13, further comprising a controller that creates a number of tasks that each consider a number of said profile record feature field groups to be profiled.

30. The system according to claim 29, wherein said controller selects and groups said profile record features for profiling via the use of partition keys, wherein the association of said partition keys to entities or said data record feature field group to be profiled will change the amount of work to be performed in each said task and the speed of operation of each said task, whereby the performance of said system is enhanced.

31. The system according to claim 30, wherein said controller performs said association of said partition keys based on a deterministic calculation against said entity or data record feature field group being mapped.

32. The system according to claim 30, wherein said controller performs said association of said partition keys based on creating equal numbers of data record feature field groups or entities for each partition key.

33. The system according to claim 30, wherein said controller performs said association of said partition keys based on recorded measures of previous tasks and operational performance of said system.

34. The system according to claim 30, wherein said controller adjusts the absolute number of said partition keys in order to improve performance of said system.

35. A method generating profile records from input records, said method comprising:

converting said input records into a data records suitable for said profile record generation; and

generating said profile records based on said data records.

36. The method according to claim 35, further comprising prioritizing and/or processing of tasks.

37. The method according to claim 35, wherein said data records each comprise at least one data field group selected from the group consisting of:

data record feature field group, data record value field group and data record reference field group.

38. The method according to claim 37, wherein said data record feature field group comprises data fields that describe a particular feature of said data record.

39. The method according to claim 38, wherein said data fields of said data record feature field group are at least one field selected from the group consisting of:

a value representing a finite time, entity, additional characteristics associated with said input record, and other possible characteristics that may be present and transformed from said input record.

40. The method according to claim 37, wherein said data record feature field group identifies and selects features for said aggregate profile record generation.

41. The method according to claim 37, wherein said data record value field group comprises data fields that describe the values associated with said data record feature field group.

42. The method according to claim 37, wherein the step of generating said profile records generates statistics for said data record value field group across a plurality of said data records during said profile record generation.

43. The method according to claim 37, wherein said data record reference field group comprises data fields that are copies or transformed from said input record and which are to be stored for reference purposes or for other non-profile record generation tasks.

44. The method according to claim 43, wherein said data fields of said data record reference field group are at least one selected from the group consisting of:

narrative.

45. The method according to claim 37, wherein said profile record is produced by aggregation or other statistical processing of said data record value fields for a particular data record feature fields present in a plurality of said data records.

46. The method according to claim 45, wherein each said profile record comprises a profile record feature field group and a profile record statistics field group.

47. The method according to claim 46, wherein said profile record feature field group corresponds to a particular field present in said data record feature field group that are considered by said statistics engine during profile generation.

48. The method according to claim 46, wherein the combination of fields in said profile record feature field group defines the characteristic of the profile, wherein said combination of fields from said profile record feature field group defines which said data records are processed by said statistics engine in order to generate said profile record.

49. The method according to claim 46, wherein said statistics field group provides derived aggregate statistics for said profile record feature field group.

50. The method according to claim 46, wherein said statistics field group is created by said statistics engine through the aggregation or other mathematical manipulation of said data records identified by a particular profile record feature field group.

51. The method according to claim 46, wherein said profile record feature field group includes at least one field selected from the group consisting of:

a value representing a finite time, entity, additional characteristics associated with said data record, and other possible characteristics that may be present and transformed from said data record.

52. The method according to claim 46, wherein said profile record statistics field group includes at least one field selected from the group consisting of:

number of said data records considered in the aggregation, the maximum values located as part of the aggregation, the minimum values located as part of the aggregation, the total sum of values located as part of the aggregation, the sum of values squared.

53. The method according to claim 36, wherein said tasks are a unit of work to be performed.

54. The method according to claim 53, wherein said work is creation of one or more said data records, and/or creation of one or more said profile records.

55. The method according to claim 36, wherein said tasks are organized via at least one task queue.

56. The method according to claim 55, wherein said task queue comprises at least one field selected from the group consisting of:

task field, a descriptor field, a priority field and a status field.

57. The method according to claim 56, wherein said tasks are ordered in said task queue based on the priority assigned to said task and selected for execution based on the status and an execution order assigned to said task.

58. The method according to claim 35, further comprising the step of creating normalized data representations during the converting said input records into a data records.

59. The method according to claim 58, wherein said step of creating normalized data representation involves performing at least one transformation selected from the group consisting of:

entity substitution, reference lookup, regular expression matching, field concatenation, hash functions, phonetic encoding, format conversions, temporal substitutions, deterministic methods, substring matching and field lookup methods.

60. The method according to claim 46, further comprising a step of creating a task for each combination of said profile record feature field group to be profiled.

61. The method according to claim 46, further comprising a step of creating a single task to profile all of said profile record feature field groups to be profiled.

62. The method according to claim 46, further comprising a step of creating a number of tasks that each consider a number of said profile record feature field groups to be profiled.

63. The method according to claim 62, wherein said step of creating selects and groups said profile record features for profiling via the use of partition keys, wherein the association of said partition keys to entities or said data record feature field group to be profiled will change the amount of work to be performed in each said task and the speed of operation of each said task, whereby the performance of said system is enhanced.

64. The method according to claim 63, wherein said step of creating performs said association of said partition keys based on a deterministic calculation against said entity or data record feature field group being mapped.

65. The method according to claim 63, wherein said step of creating performs said association of said partition keys based on creating equal numbers of data record feature field groups or entities for each partition key.

66. The method according to claim 63, wherein said step of creating performs said association of said partition keys based on recorded measures of previous tasks and operational performance of said system.

67. The method according to claim 63, wherein said step of creating adjusts the absolute number of said partition keys in order to improve performance of said system.

68. A task control method that sub-divides the aggregations of profile records into units of work that can be individually performed, said method comprising:

partitioning based on a pre-determined partitioning key associated with entities to be profiled, wherein the association between said partitioning key and said entities being profiled is varied in order to optimize the profiling performance.

69. The method according to claim 68, wherein the variation of the association between said entities being profiled and said partitioning key is controlled based on previously calculated aggregate profile statistics.

70. The method according to claim 68, wherein the variation of the association between said entities being profiled and said partitioning key is controlled based on known runtime performance.

71. The method according to claim 68, wherein the variation of the association between said entities being profiled and said partitioning key is controlled based on a combination of runtime performance and the on previously calculated aggregate profile statistics.