Methods and systems for calculating and retrieving analytic data

• US 9,940,385 B2
• Filed: 09/29/2016
• Issued: 04/10/2018
• Est. Priority Date: 03/04/2013
• Status: Active Grant

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Jones, William C.
• Primary Examiner(s): Saeed, Usmaan
• Assistant Examiner(s): Weinrich, Brian E.
• Application Number:
  US15/280,659
• Publication Number:
  US 20170017713A1
• Time in Patent Office: 558 Days
• Field of Search: 707769, 707602
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/282 Hierarchical databases, e.g. IMS, LDAP data stores or Lotus Notes
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/1093 Calendar-based scheduling for persons or groups
• CPC Class Codes: G06Q 20/22 Payment schemes or models

Abstract

A data management system for calculating and retrieving analytic data involves a computing device coupled to a database. The system includes a measure data structure including a measure-data element, a collection data structure including a collection-data element, and a fact data structure configured to store a fact-data element which includes an aggregated value, a link to the measure-data element, and a link to the collection-data element. The data structures may be, for example, database table structures. The system also includes a fact-analyzer module configured to generate the aggregated value (i.e., a result generated by a query computation performed on the database). The measure-data element defines how the aggregated value is calculated and the collection-data element defines a categorization of the aggregated value. The fact-analyzer module is also configured to store the aggregated value and links to the measure-data element and the collection-data element in the fact-data element.

Claims

1. A data management system for calculating and retrieving analytic data includes a computing device coupled to a database, the data management system comprising:

a measure data structure within the database comprising a measure-data element;

a collection data structure within the database comprising a collection-data element;

a calendar data structure within the database comprising a calendar-data element;

a fact data structure within the database configured to store a fact-data element, wherein the fact-data element includes an aggregated value; and

a fact-analyzer module configured to;

calculate the aggregated value from the database based at least in part on the measure-data element, the collection-data element, and the calendar-data element, wherein the calendar-data element defines a time range for the calculation;

store the aggregated value, wherein the fact-data element further includes a link to the calendar-data element;

receive, from a user, a request for the aggregated value after storing the fact-data element, wherein receiving a request for the aggregated value includes i) identifying the fact-data element using at least the measure-data and element, the collection-data element, and the calendar-data element, and ii) using a database cross-table operation including a join operation and a decode operation, such that each of the fact data structure, measure data structure, collection data structure, and calendar data structure are referenced during the request, and wherein a point of view filter is used during identification of the fact-data element to prevent retrieval of duplicate information and to assure that the aggregated value pertains to the user making the request;

identify the aggregated value from the fact data structure based at least in part on the request; and

output the aggregated value.

2. The system in accordance with claim 1, wherein the fact-analyzer module further configured to:

recalculate the aggregated value after the aggregated value becomes stale, wherein a stale aggregated value is determined based on at least one of an elapsed amount of time, a fixed point in time, and a user input; and

store the recalculated aggregated value in the fact-data element.

3. The system in accordance with claim 1, wherein the fact-analyzer module is further configured to store at least one of an additional fact-data element, an additional measure-data element, and an additional collection-data element to the database.

4. The system in accordance with claim 1, wherein generating an aggregated value further utilizes another collection-data element from the collection data structure to define an additional categorization of the aggregated value.

5. The system in accordance with claim 1, wherein the collection data structure includes a collection type, a collection identifier, a collection name, a collection attribute, and a collection entity.

6. The system in accordance with claim 1, wherein the measure data structure includes a measure name, a measure identifier, a metric type, and a metric element.

7. The system in accordance with claim 1, wherein the calendar data structure includes a calendar identifier, a calendar item identifier, a calendar item name, a start date, and an end date.

8. A method of calculating and retrieving analytic data stored within a computing device in communication with a database, the method comprising the steps of:

storing a measure-data element in a measure data structure within the database;

storing a collection-data element in a collection data structure within the database;

storing a calendar-data element in a calendar data structure within the database;

calculating an aggregated value from the database based at least in part on the measure-data element, the collection-data element, and the calendar-data element, wherein the calendar-data element defines a time range for the calculation;

storing the aggregated value in a fact-data element of a fact data structure, and storing a link to the calendar-data element in the fact-data element;

receiving a request identifying the fact-data element from the fact data structure after storing the fact-data element, wherein receiving a request identifying the fact-data element comprises i) identifying the fact-data element using at least the measure-data element, the collection-data element, and the calendar-data element, and ii) using a database cross-table operation having a join operation and a decode operation, such that each of the fact data structure, measure data structure, collection data structure, and calendar data structure are each referenced during the request, and wherein a point of view filter is used during identification of the fact-data element to prevent retrieval of duplicate information and to assure that the aggregated value pertains to the user making the request; and

outputting the aggregated value from the fact-data element.

9. The method in accordance with claim 8, the method further comprising the steps of:

recalculating the aggregated value after the aggregated value becomes stale, wherein a stale aggregated value is determined based on at least one of an elapsed amount of time, a fixed point in time, and a user input; and

storing the recalculated aggregated value in the fact-data element.

10. The method in accordance with claim 8, the method further comprising the step of storing at least one of an additional fact-data element, an additional measure-data element, and an additional collection-data element to the database.

11. The method in accordance with claim 8, wherein calculating an aggregated value further includes utilizing another collection-data element from the collection data structure to define an additional categorization of the aggregated value.

12. The method in accordance with claim 8, wherein storing a collection-data element includes storing a collection type, a collection identifier, a collection name, a collection attribute, and a collection entity.

13. The method in accordance with claim 8, wherein storing a measure-data element includes storing a measure name, a measure identifier, a metric type, and a metric element.

14. The method in accordance with claim 8, wherein storing a calendar-data element includes storing a calendar identifier, a calendar item identifier, a calendar item name, a start date, and an end date.

15. Computer-readable storage media having computer-executable instructions embodied thereon, wherein, when executed by at least one processor, the computer-executable instructions cause the processor to:

store a measure-data element in a measure data structure within a database;

store a collection-data element in a collection data structure within the database;

store a calendar-data element in a calendar data structure within the database;

calculate an aggregated value from the database based at least in part on the measure-data element, the collection-data element, and the calendar-data element, wherein the calendar-data element defines a time range for the calculation;

store the aggregated value in a fact-data element of a fact data structure and store a link to the calendar-data element in the fact-data element;

receive a request identifying the fact-data element from the fact data structure after storing the fact-data element, wherein receiving a request includes i) identifying the fact-data element using at least the measure-data element, the collection-data element, and the calendar-data element, and ii) using a database cross-table operation including a join operation and a decode operation, such that each of the fact data structure, measure data structure, collection data structure, and calendar data structure are each referenced during the request, and wherein a point of view filter is used during identification of the fact-data element to prevent retrieval of duplicate information and to assure that the aggregated value pertains to the user making the request; and

output the aggregated value from the fact-data element.