Linking discrete dimensions to enhance dimensional analysis
Not all facts in a data warehouse are described by the same set of dimensions. However, there can be associations between the data dimensions and other dimensions. By maintaining a set of relationships that are capable of linking the dimensional keys used in existing data to the keys of an associated dimension, a data transformation can be constructed that summarizes by the original and by the associated dimensions in feeds in an analytical data mart (cube) that includes all the dimensions. This cube can then be consolidated and analyzed in a slice-and-dice fashion as though all the dimensions were independent. Data transformed in this manner can be analyzed alongside data from a source that is keyed by all of the dimensions.
1. A method of linking one or more discrete dimensions through a use of a constructed multidimensional cube to improve performance of computer processing operations, the method comprising:
storing, by a computer, base data in a format comprising one or more original dimension keys in a database;
comparing, by the computer, one or more records of the base data with criteria of one or more rule sets to determine whether one or more records of the base data meet the criteria of the one or more rule sets;
tagging, by the computer, one or more records of the base data that meet the criteria of the one or more rule sets with one or more additional dimension keys;
aggregating, by the computer, a single resultant set comprising all of the one or more original dimension keys and all of the one or more additional dimension keys;
constructing a multidimensional cube with the one or more original dimension keys and the one or more additional dimension keys, thereby reducing computational run time of the computer; and
storing the multidimensional cube in a storage device.
2. The method of claim 1 , further comprising:
providing, by the computer, a set of templates that define the one or more rule sets, the one or more rule sets identify the criteria in the base data for subsequent tagging.
3. The method of claim 1 , further comprising:
performing, by the computer, analysis and consolidation through dimensions of the multidimensional cube as though the dimensions are independent.
4. The method of claim 1 , wherein one or more fact tables are source data for the multidimensional cube.
5. The method of claim 1 , wherein the multidimensional cube further comprises an OLAP cube.
6. The method of claim 1 , further comprising:
converting, by the computer, the one or more records of the base data to ensure inter-consistency and compatibility with the one or more rule sets.
7. The method of claim 1 , further comprising:
performing, by the computer, an analysis of the multidimensional cube; and
modifying, by the computer, an existing dimension based on a portion of the analysis.
8. A non-transitory computer-readable storage media comprising processor executable instructions that when executed improves performance of computer processing operations and is configured to:
store base data in a format comprising one or more dimension keys in a database;
compare one or more records of the base data with criteria of one or more rule sets to determine whether the one or more records of the base data meet the criteria of the one or more rule sets;
tag one or more records of the base data that meet the criteria of the one or more rule sets with one or more additional dimension keys;
aggregate a single resultant set comprising all of the one or more dimension keys and all of the one or more additional dimension keys;
construct a multidimensional cube with the one or more original dimension keys and the one or more additional dimension keys, thereby reducing computational run time of the computer; and
store the multidimensional cube in a storage device.
9. The non-transitory computer-readable storage media of claim 8 , wherein the processor executable instructions when executed are further configured to:
provide a set of templates that define the one or more rule sets, the one or more rule sets identify the criteria in the base data for subsequent tagging.
10. The non-transitory computer-readable storage media of claim 8 , wherein the processor executable instructions when executed are further configured to:
perform analysis and consolidation through dimensions of the multidimensional cube as though the dimensions are independent.
11. The non-transitory computer-readable storage media of claim 8 , wherein one or more fact tables are source data for the multidimensional cube.
12. The non-transitory computer-readable storage media of claim 8 , wherein the multidimensional cube further comprises an OLAP cube.
13. The non-transitory computer-readable storage media of claim 8 , wherein the processor executable instructions when executed are further configured to:
convert the one or more records of the base data to ensure inter-consistency and compatibility with the one or more rule sets.
14. The non-transitory computer-readable storage media of claim 8 , wherein the processor executable instructions when executed are further configured to:
perform an analysis of the multidimensional cube; and
modify an existing dimension based on a portion of the analysis.
15. A system of linking one or more discrete dimensions through a use of a constructed multidimensional cube to improve performance of computer processing operations, the system comprising:
one or more computers comprising a processor and a memory, the one or more computers configured to:
store base data in a format comprising one or more dimension keys in a database;
compare one or more records of the base data with criteria of one or more rule sets to determine whether the one or more records of the base data meet the criteria of the one or more rule sets;
tag one or more records of the base data that meet the criteria of the one or more rule sets with one or more additional dimension keys;
aggregate a single resultant set comprising all of the one or more dimension keys and all of the one or more additional dimension keys;
construct a multidimensional cube with the one or more original dimension keys and the one or more additional dimension keys, thereby reducing computational run time of the computer; and
store the multidimensional cube in a storage device.
16. The system of claim 15 , wherein the one or more computers are further configured to:
provide a set of templates that define the one or more rule sets, the one or more rule sets identify the criteria in the base data for subsequent tagging.
17. The system of claim 15 , wherein the one or more computers are further configured to:
perform analysis and consolidation through dimensions of the multidimensional cube as though the dimensions are independent.
18. The system of claim 15 , wherein one or more fact tables are source data for the multidimensional cube.
19. The system of claim 15 , wherein the multidimensional cube further comprises an OLAP cube.
20. The system of claim 15 , wherein the one or more computers are further configured to:
convert the one or more records of the base data to ensure inter-consistency and compatibility with the one or more rule sets.