Business intelligent architecture system and method

• US 7,580,944 B2
• Filed: 07/27/2006
• Issued: 08/25/2009
• Est. Priority Date: 07/27/2006
• Status: Expired due to Fees

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Zhuge, Yue, Alnahlawi, Mahmoud
• Primary Examiner(s): Cottingham; John R.
• Assistant Examiner(s): Bromell; Alexandria Y
• Application Number:
  US11/493,950
• Publication Number:
  US 20080027970A1
• Time in Patent Office: 1,125 Days
• Field of Search: 707/103.R, 707/102, 707/101
• US Class Current: 1/1
• CPC Class Codes: G06F 16/284 Relational databases
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface
• CPC Class Codes: Y10S 707/99944 Object-oriented database structure

Abstract

The present disclosure describes a standardized logical model architecture, for use in building a business intelligent systems, and a system and method of using the logical model architecture to populate a data repository (e.g., a data mart/warehouse) with the data to satisfy reporting and data analysis needs.

Claims

1. A method comprising the steps of:

defining a first interface of a logical model architecture comprising one or more dimensions and/or measures;

defining a second interface of said logical model architecture comprising metrics, and associations between said metrics and said one or more dimensions and/or measures;

defining a third interface of said logical model architecture comprising a plurality of user data requirements corresponding to at least one of said metrics and at least one of said dimensions and/or measures;

defining, a first set of transformations of said logical model architecture via a computing device, comprising one or more transformations to map source data items to said one or more dimensions and/or measures of said first interface;

defining, a second set of transformations of said logical model architecture via the computing device, each transformation of said second transformation set corresponding to at least one of said user data requirements and communicated to a data repository maintained on a computer-readable storage medium for populating the data repository with data to satisfy said corresponding user data requirement;

defining, via the computing device, at least one star graph having nodes, each node corresponding to a star identifying at least one of said dimensions, each of said dimensions identified in said star belonging to a dimension hierarchy, an edge between two nodes representing one or more aggregation operations such that each node corresponds to a level of aggregation for each of said dimensions identified by said star; and

traversing the star graph so as to determine said level of aggregation level for a metric corresponding to star.

2. The method of claim 1, wherein each of said dimensions represents one of said source data items by which at least one other of said source data items can be grouped.

3. The method of claim 1, wherein each of said measures represents one of said source data items other than those data items represented by said dimensions.

4. The method of claim 1, wherein in said second interface of said logical model, a metric corresponds to a quantifiable piece of information usable in a business intelligent system.

5. The method of claim 1, wherein said second interface at least one of said associations comprises a metric-star, said metric-star identifying one of said metrics and one or more of said dimensions by which said identified metric can be grouped.

6. The method of claim 5, wherein each of said one or more dimensions identified by said metric-star association belongs to a dimension hierarchy having a most granular dimension at a lowest level of the hierarchy, said dimension identified by said metric-star being said most granular dimension.

7. The method of claim 1, wherein in said third interface at least one of said user data requirements comprises a metric-star, said metric-star identifying one of said metrics and one or more of said dimensions by which said identified metric can be grouped.

8. The method of claim 7, wherein each of said one or more dimensions identified by said metric-star belongs to a dimension hierarchy having a most granular dimension at a lowest level of the hierarchy with one or more ancestor dimensions of the most granular dimension, said dimension identified by said metric-star being said most granular dimension or one of said ancestor dimensions.

9. The method of claim 1, wherein in said third interface a metric identified in one of said user data requirements corresponds to one or more output data items of a business intelligent system.

10. The method of claim 9, wherein said business intelligent system comprises reporting and analysis tools which operate on said data repository.

11. The method of claim 1, wherein said one or more aggregation operations comprise a roll-up operation to roll a child dimension into its parent dimension.

12. The method of claim 11, wherein said roll-up operation operates the define a level of aggregation for a metric associated with said child and parent dimensions, such that said associated metric is grouped by said parent dimension.

13. The method of claim 11, wherein said roll-up operation operates to define a level of aggregation corresponding to one or more of said source data items used to determine said metric.