KStore data analyzer

• US 20060101048A1
• Filed: 08/26/2005
• Published: 05/11/2006
• Est. Priority Date: 11/08/2004
• Status: Abandoned Application

• Current Assignee: Unisys Corporation
• Original Assignee: Unisys Corporation
• Inventors: Claar, Jane Van Keuren, Mazzagatti, Jane Campbell, Phan, Tony T., Didizian, Haig C.
• Application Number:
  US11/212,339
• Publication Number:
  US 20060101048A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2246 Trees, e.g. B+trees

Abstract

A data analysis system for performing an analytic to obtain an analytic result in a computing device having memory including a data analyzer interface, at least one interlocking trees datastore within the associated memory, and at least one analytic application executed. The data analysis system of the invention also includes a plurality of interlocking trees datastores wherein the at least one interlocking trees datastore is selected from the plurality of interlocking trees datastores in accordance with the data analyzer interface. The system can include a plurality of data sources wherein the at least one interlocking trees datastore is created from a data source selected from the plurality of data sources in accordance with the data analyzer interface. The at least one interlocking trees datastore further can be a static interlocking trees datastore or a dynamic interlocking trees datastore. The at least one interlocking trees datastore continuously records new data.

Claims

1. A data analysis system for performing an analytic to obtain an analytic result in a computing device having memory associated therewith, said data analysis system comprising:

a data analyzer interface,

at least one interlocking trees datastore within said associated memory of said computing device, and

at least one analytic application executed by said computing device.

2. The data analysis system of claim 1, further comprising a plurality of interlocking trees datastores wherein said at least one interlocking trees datastore is selected from said plurality of interlocking trees datastores in accordance with said data analyzer interface.

3. The data analysis system of claim 1, further comprising a plurality of data sources wherein said at least one interlocking trees datastore is created from a data source selected from said plurality of data sources in accordance with said data analyzer interface.

4. The data analysis system of claim 1, wherein said at least one interlocking trees datastore further comprises a static interlocking trees datastore.

5. The data analysis system of claim 1, wherein said at least one interlocking trees datastore comprises a dynamic interlocking trees datastore.

6. The data analysis system of claim 5, wherein said at least one interlocking trees datastore continuously records new data.

7. The data analysis system of claim 5, wherein said at least one interlocking trees datastore includes records of data and said at least one interlocking trees datastore continuously receives updates of said records of data.

8. The data analysis system of claim 1, including a plurality of analytic applications wherein said at least one analytic application is selected from said plurality of analytic applications in accordance with said data analyzer interface.

9. The data analysis system of claim 8, wherein said at least one analytic application analyzes a static interlocking trees datastore.

10. The data analysis system of claim 8, wherein said at least one analytic application analyzes a dynamic interlocking trees datastore.

11. The data analysis system of claim 8, wherein said at least one analytic application further comprises any type of analytic.

12. The data analysis system of claim 11, wherein said at least one analytic application further comprises an accounting/mathematical functional category analytic.

13. The data analysis system of claim 12, wherein said at least one analytic application further comprises a sum analytic.

14. The data analysis system of claim 11, wherein said at least one analytic application further comprises a statistical functional category analytic.

15. The data analysis system of claim 11, wherein said at least one analytic application further comprises a classification functional category analytic.

16. The data analysis system of claim 11, wherein said at least one analytic application further comprises a relationship functional category analytic.

17. The data analysis system of claim 11, wherein said at least one analytic application further comprises a visualization functional category analytic.

18. The data analysis system of claim 11, wherein said at least one analytic application further comprises a statistical functional category analytic.

19. The data analysis system of claim 11, wherein said at least one analytic application further comprises a meta-data functional category analytic.

20. The data analysis system of claim 12, wherein said at least one analytic application comprises a further functional category analytic.

21. The data analysis system of claim 1, wherein said data analyzer interface provides access to at least one administration application.

22. A data analysis method for performing an analytic to obtain an analytic result in a data processing device having a memory associated therewith, said method comprising:

providing a data analyzer interface for said data processing device,

storing at least one interlocking trees datastore in said memory of said data processing device, and

executing at least one analytic application in accordance with said at least one interlocking trees datastore.

23. The data analysis method of claim 22, wherein said associated memory of said data processing device includes a plurality of interlocking trees datastores further comprising selecting said at least one interlocking trees datastore from said plurality of interlocking trees datastores in accordance with said data analyzer interface.

24. The data analysis method of claim 22, wherein said data processing device includes a plurality of data sources further comprising creating said at least one interlocking trees datastore from a data source selected from said plurality of data sources in accordance with said data analyzer interface.

25. The data analysis method of claim 22, wherein said data processing device includes a plurality of analytic applications further comprising selecting said at least one analytic application from said plurality of analytic applications in accordance with said data analyzer interface.

26. A method of performing an analytic to obtain an analytic result in a KStore having a plurality of K paths each K path of said plurality of K paths having end nodes, comprising:

determining at least one KStore parameter in accordance with at least one K path of said plurality of K paths to provide at least one determined parameter; and

obtaining said analytic result in accordance with said determined at least one determined parameter.

27. The method of performing an analytic to obtain an analytic result of claim 26, wherein said at least one KStore result comprises a count.

28. The method of performing an analytic to obtain an analytic result of claim 26, wherein said at least one KStore result comprises a value.

29. The method of performing an analytic to obtain an analytic result of claim 26, wherein said at least one KStore result comprises sequence information.

30. The method of performing an analytic to obtain an analytic result of claim 26, comprising constraining said KStore with at least one constraint to provide at least one selected K path from said plurality of K paths.

31. The method of performing an analytic to obtain an analytic result of claim 30, wherein said constraining provides a set of selected K paths comprising applying at least one focus to said KStore to provide a further set of selected K paths.

32. The method of performing an analytic to obtain an analytic result of claim 26, wherein said analytic is an analytic for analyzing a dynamic KStore.

33. The method of performing an analytic to obtain an analytic result of claim 26, wherein said analytic comprises an accounting/mathematical functional category analytic.

34. The method of performing an analytic to obtain an analytic result of claim 33, wherein said analytic is a sum analytic and said analytic result comprises a sum of a plurality of parameters.

35. The method of performing an analytic to obtain an analytic result of claim 34, a set of selected K paths further comprising:

constraining said KStore to provide a set of selected K paths;

determining a plurality of said KStore results in accordance with said set of selected K paths; and

summing said KStore parameters of said plurality of KStore parameters.

36. The method of performing an analytic to obtain an analytic result of claim 35, further comprising traversing said K paths of said set of K paths to determine said plurality of KStore parameters.

37. The method of performing an analytic to obtain an analytic result of claim 36, further comprising:

traversing said K paths of said set of K paths to the respective end nodes of said K paths of said set of selected K paths; and

determining said plurality of KStore parameters in accordance with said respective end nodes.

38. The method of performing an analytic to obtain an analytic result of claim 37, further comprising:

determining a count of each K path of said set of K paths to provide a plurality of determined counts; and

summing said determined counts to provide said analytic result.

39. The method of performing an analytic to obtain an analytic result of claim 33, wherein said analytic is a distinct count analytic and said analytic result is a count of at least one distinct parameter in said KStore.

40. The method of performing an analytic to obtain an analytic result of claim 39, further comprising:

constraining said KStore to provide a set of selected K paths;

determining the number of times said distinct parameter occurs within said set of K paths.

41. The method of performing an analytic to obtain an analytic result of claim 40, further comprising:

determining a plurality of distinct parameters; and

determining the number of times each distinct value of said plurality of distinct parameters occurs within said set of K paths.

42. The method of performing an analytic to obtain an analytic result of claim 41, further comprising:

performing distinct parameter traversals of said K paths of said set of K paths; and

determining said number of times said distinct parameters are encountered in accordance with said distinct value traversals.

43. The method of performing an analytic to obtain an analytic result of claim 40, further comprising applying a further constraint to said KStore prior to determine said number of times said distinct value occurs.

44. The method of performing an analytic to obtain an analytic result of claim 40, further comprising applying a focus variable to said KStore prior to determining said number of times said distinct parameter occurs.

45. The method of performing an analytic to obtain an analytic result of claim 33, wherein said analytic comprises a data aggregation analytic and said analytic result is aggregated data.

46. The method of performing an analytic to obtain an analytic result of claim 33, wherein said analytic comprises the accounting/mathematical functional category analytics other than those in the group consisting of the sum analytic, the distinct group analytic and the aggregated data analytic.

47. The method of performing an analytic to obtain an analytic result of claim 46, further comprising:

constraining said KStore to provide a set of selected K paths; and

traversing at least one K path of said set of selected K paths.

48. The method of performing an analytic to obtain an analytic result of claim 26, wherein said analytic comprises a statistical functional category of analytics.

49. The method of performing an analytic to obtain an analytic result of claim 48, wherein said analytic comprises a single variable prediction analytic.

50. The method of performing an analytic to obtain an analytic result of claim 49, further comprising:

applying a focus variable to said KStore; and

determining a probability in accordance with said focus variable.

51. The method of performing an analytic to obtain an analytic result of claim 50, further comprising:

constraining said KStore to provide a set of selected K paths; and

determining a distinct count of said focus variable within said set of selected K paths.

52. The method of performing an analytic to obtain an analytic result of claim 51, further comprising determining said probability in accordance with the number of selected K paths in said set of selected K paths.

53. The method of performing an analytic to obtain an analytic result of claim 51, further comprising determining said probability in accordance with the number of K paths in said plurality of selected K paths.

54. The method of performing an analytic to obtain an analytic result of claim 51, wherein said determining of said distinct count further comprises:

performing distinct count traversals of said K paths of set of selected K paths; and

counting the number of times said focus variable is encountered during said distinct count traversals.

55. The method of performing an analytic to obtain an analytic result of claim 48, wherein said analytic comprises all further statistical functional category analytics other than those in the group consisting of the single variable prediction analytic.

56. The method of performing an analytic to obtain an analytic result of claim 55, further comprising:

constraining said KStore to provide a set of selected K paths;

traversing at least one K path of said set of selected K paths.

57. The method of performing an analytic to obtain an analytic result of claim 26, wherein said analytic comprises a classificational functional category analytic.

58. The method of performing an analytic to obtain an analytic result of claim 57, wherein said analytic is a contented classification analytic and said analytic result is a classification of a sample within a context.

59. The method of performing an analytic to obtain an analytic result of claim 58, wherein the sample contains sample variables comprising constraining said KStore with said sample variables.

60. The method of performing an analytic to obtain an analytic result of claim 57, wherein said analytic is a dynamic decision free analytic with said analytic result is a hierarchical tree representation of a data set.

61. The method of performing an analytic to obtain an analytic result of claim 60, wherein said hierarchical tree representation comprises a single root node and a plurality of branches beginning with said single root node.

62. The method of performing an analytic to obtain an analytic result of claim 57, wherein said analytic comprises a Bayes classification analytic and said analytic result is a probability.

63. The method of performing an analytic to obtain an analytic resultof claim 62, wherein said analytic result comprises a probabilistic classification.

64. The method of performing an analytic to obtain an analytic result of claim 26, wherein said analytic comprises a relationship functional category analytic.

65. The method of performing an analytic to obtain an analytic result of claim 64, wherein said analytic comprises an associated rules category analytic and said analytic result is a probability.

66. The method of performing an analytic to obtain an analytic result of claim 65, wherein said probability comprises a probability of a variable co-occurring with a focus variable.

67. The method of performing an analytic to obtain an analytic result of claim 66, wherein said analytic is a market basket analytic and an analytic result is a list of items that are frequently grouped together.

68. The method of performing an analytic to obtain an analytic result of claim 67, comprising determining said list of items in accordance with a list of sales transactions.

69. The method of performing an analytic to obtain an analytic result of claim 26, wherein said analytic comprises a visualizational category analytic.

70. The method of performing an analytic to obtain an analytic result of claim 69, wherein said analytic comprises a chart generator analytic.

71. The method of performing an analytic to obtain an analytic result of claim 69, wherein said analytic comprises a field chart analytic.

72. The method of performing an analytic to obtain an analytic result of claim 26, wherein said analytic comprises a meta-data functional category analytic.

73. The method of performing an analytic to obtain an analytic result of claim 26, wherein said analytic comprises all further analytics in categories other than the accounting/mathematical functional category, the statistical functional category, the classification functional category, the relationship functional category, the visualization functional category and the meta-data functional category.

74. A KStore system for performing an analytic to obtain an analytic result, comprising:

a data analyzer

a data source selected by said data analyzer; and

an analytic application selected by said data analyzer.

75. The KStore system for performing an analytic of claim 74, wherein said KStore system includes a plurality of data sources further comprising a selected data source selected from said plurality of data sources by said data analyzer.

76. The KStore system for performing an analytic of claim 74, wherein said KStore system includes a plurality of analytic applications further comprising a selected analytic application selected from said plurality of analytic applications by said data analyzer.

77. The KStore system for performing an analytic of claim 74, wherein said KStore system includes a plurality of data sources and a plurality of analytics further comprising a selected data source selected from said plurality of data sources by said data analyzer and a selected analytic application selected from said plurality of analytic applications by said data analyzer.

78. The KStore system for performing an analytic of claim 77, wherein said KStore system includes an API utility for providing instructions to said data analyzer regarding the selection of at least one of said selected data source or said selected analytic application.

79. The KStore system for performing an analytic of claim 77, wherein said selected analytic comprises an analytic from the accounting/mathematical functional category of analytics.

80. The KStore system for performing an analytic of claim 77, wherein said selected analytic comprises an analytic from the statistical functional category of analytics.

81. The KStore system for performing an analytic of claim 77, wherein said selected analytic comprises an analytic from the classification functional category of analytics.

82. The KStore system for performing an analytic of claim 77, wherein said selected analytic comprises an analytic from the relationship functional category of analytics.

83. The KStore system for performing an analytic of claim 77, wherein said selected analytic comprises an analytic from the visualization functional category of analytics.

84. The KStore system for performing an analytic of claim 77, wherein said selected analytic comprises an analytic from the meta-data functional category of analytics.

85. The KStore system for performing an analytic of claim 74, further comprising storage for storing at least one category of analytics and the members of said at least one category.