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 the use of a constructed multidimensional cube to improve performance of computer processing operations, the method comprising:
collecting, by a computer, base data from one or more entities;
storing, by the computer, the base data in a format comprising one or more dimension keys in a database;
providing, by the computer, a set of templates that define one or more rule sets, the one or more rule sets identify criteria in the base data for subsequent tagging and associating an instance of at least one of the one or more dimension keys with an instance of at least one of one or more additional dimension keys;
when base data is incompatible with the one or more rule sets defined by the set of templates, translating, by the computer, base data to a compatible data structure;
comparing, by the computer, one or more records of the base data with the criteria of the 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, wherein meeting the criteria of the one or more rule sets triggers a tagging of the one or more records;
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 dimension keys and all of the one or more additional dimension keys, the single resultant set aggregated without rebuilding the format comprising one or more dimension keys in the database;
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 adding another dimension key.
3. The method of claim 1 , further comprising modifying the one or more additional dimension keys.
4. The method of claim 1 , further comprising modifying the one or more original dimension keys.
5. The method of claim 1 , further comprising viewing the stored multidimensional cube from one or more different viewpoints.
6. The method of claim 1 , further comprising providing a user interface where one or more different dimensions can be selected.
7. The method of claim 1 , wherein a dimension is seasonality.
8. The method of claim 1 , wherein the base data includes information comprising one or more of SKU, outlet, time frame, sales, price and date.
9. A non-transitory computer-readable storage media comprising processor executable instructions that when executed improves performance of computer processing operations and is configured to:
collect base data from one or more entities;
store the base data in a format comprising one or more dimension keys in a database;
provide a set of templates that define one or more rule sets, the one or more rule sets identify criteria in the base data for subsequent tagging and associating an instance of at least one of the one or more dimension keys with an instance of at least one of one or more additional dimension keys;
when base data is incompatible with the one or more rule sets defined by the set of templates, translate base data to a compatible data structure;
compare one or more records of the base data with the criteria of the 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, wherein meeting the criteria of the one or more rule sets triggers a tagging of the one or more records;
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, the single resultant set aggregated without rebuilding the format comprising one or more dimension keys in the database;
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.
10. The method of claim 1 , further comprising determining and generating multiple different viewpoints based on a user request.
11. A system of linking one or more discrete dimensions through the 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:
collect base data from one or more entities and stores the base data in a format comprising one or more dimension keys;
provide a set of templates that define one or more rule sets, the one or more rule sets identify criteria in the base data for subsequent tagging and associating an instance of at least one of the one or more dimension keys with an instance of at least one of one or more additional dimension keys;
when base data is incompatible with the one or more rule sets defined by the set of templates, translate base data to a compatible data structure;
compare one or more records of the base data with the criteria of the 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, wherein meeting the criteria of the one or more rule sets triggers a tagging of the one or more records;
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, the single resultant set aggregated without rebuilding the stored base data format comprising one or more dimension keys; and
construct and store in a storage device 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.
12. The system of claim 11 , further comprising a plug-n-play dimension module that allows adding dimension keys.
13. The system of claim 11 , wherein a plug-n-play dimension module allows modification of the one or more additional dimension keys.
14. The system of claim 11 , wherein a plug-n-play dimension module allows modification of the one or more original dimension keys.
15. The system of claim 11 , wherein the multidimensional cube can be viewed on a user interface from one or more different viewpoints.
16. The system of claim 11 , further comprising a user interface that allows different dimensions to be selected.
17. The system of claim 11 , wherein a dimension is seasonality.
18. The method of claim 11 , wherein the base data includes information comprising one or more of SKU, outlet, time frame, sales, price and date.
19. A method of creating a data structure representing product or service information in the form of a cube having dimensions to improve performance of computer processing operations, the method comprising:
receiving base data from one or more information sources;
providing a set of templates that define one or more rule sets, the one or more rule sets identify criteria in the base data for subsequent tagging and associating an instance of at least one of the one or more dimension keys with an instance of at least one of one or more additional dimension keys;
when base data is incompatible with the one or more rule sets defined by the set of templates, translating base data to a compatible data structure;
comparing one or more records of the base data with the criteria of the 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, wherein meeting the criteria of the one or more rule sets triggers a tagging of the one or more records;
tagging 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;
constructing and storing in memory a multidimensional cube including the one or more additional dimension keys, without rebuilding the information source, thereby reducing computational run time of the computer; and
providing the multidimensional cube for slice-and-dice analysis.