Domain independent system and method of automating data aggregation

A computer automated method of aggregating data, the method comprising the steps of:

• inputting a set of user-defined instructions into a computer database system;
• inputting a user query into the computer database system;
• mining the computer database system for data relevant to the user query;
• creating a data set comprising the data relevant to the user query;
• and
• aggregating data in the data set using domain metrics selected based on any of predefined and configurable rules and past user usage, wherein the aggregation comprises:
• tagging all data attributes in the data set based on database metadata and inputs from a user, wherein the data attributes comprise any of data identifications (IDs), data grouping attributes, and data measure attributes, wherein the tagging process comprises inputting the user query, the database metadata for the data attributes in the user query, and attributes specifications;
• and
• reducing the number of the tagged data attributes in the data set by logically eliminating data attributes;
• wherein for each of the data attributes in the user query, the tagging process comprises tagging the data attribute as a grouping attribute when the data attribute is to be treated as a grouping attribute based on inputs to any of the computer database system and the database metadata;
• and
• wherein when the data attribute comprises a grouping attribute and has a number of unique values less than the maximum numbers of unique values allowed to select a database attribute as a grouping attribute, the tagging process comprises tagging the data attribute as a grouping attribute.

A program storage device readable by computer, tangibly embodying a program of instructions executable by said computer to perform an automated method of aggregating and presenting data, said method comprising the steps of:

• inputting a set of user-defined instructions into a computer database system;
• inputting a user query into said computer database system;
• mining said computer database system for data relevant to said user query;
• creating a data set comprising said data relevant to said user query;
• and
• aggregating data in said data set using domain metrics selected based on any of predefined and configurable rules and past user usage, wherein the aggregation comprises:
• tagging all data attributes in said data set based on database metadata and inputs from a user, wherein said data attributes comprise any of data identifications (IDs), data grouping attributes, and data measure attributes, wherein the tagging process comprises inputting said user query, said database metadata for said data attributes in said user query, and attributes specifications;
• and
• reducing the number of the tagged data attributes in said data set by logically eliminating data attributes;
• wherein for each of said data attributes in said user query, said tagging process comprises tagging the data attribute as a grouping attribute when said data attribute is to be treated as a grouping attribute based on inputs to any of said computer database system and said database metadata;
• and
• wherein when said data attribute comprises a grouping attribute and has a number of unique values less than the maximum numbers of unique values allowed to select a database attribute as a grouping attribute, said tagging process comprises tagging said data attribute as a grouping attribute.

A system of aggregating and presenting data, said system comprising:

• a user interface adapted to have a set of user-defined instructions and a user query input therein;
• a computer database system adapted to be mined for data relevant to said user query;
• a data set comprising said data relevant to said user query;
• and
• a logic component adapted to aggregate data in said data set using domain metrics selected based on any of predefined and configurable rules and past user usage, wherein said logic component adapted to aggregate said data comprises:
• a first processing unit adapted to tag all data attributes in said data set based on database metadata and inputs from a user, wherein said data attributes comprise any of data identifications (IDs), data grouping attributes, and data measure attributes, wherein said first processing unit is adapted to have said user query, said database metadata for said data attributes in said user query, and attributes specifications being input therein;
• and
• a second processing unit adapted to reduce the number of the tagged data attributes in said data set by logically eliminating data attributes;
• wherein for each of said data attributes in said user query, said first processing unit is adapted to tag the data attribute as an ID when said attribute is to be treated as an ID based on inputs to any of said computer database system and said database metadata, wherein for each of said data attributes in said user query, said first processing unit is adapted to apply default statistics when user specified statistics are unavailable and tag the data attribute as a measure when said data attribute is to be treated as a measure based on inputs to any of said computer database system and said database metadata, wherein for each of said data attributes in said user query, said first processing unit is adapted to tag the data attribute as a grouping attribute when said data attribute is to be treated as a grouping attribute based on inputs to any of said computer database system and said database metadata, wherein when said data attribute comprises a grouping attribute and has a number of unique values less than the maximum number of unique values allowed to select a database attribute as a grouping attribute, said first processing unit being adapted to tag said data attribute as a grouping attribute, wherein said first processing unit is adapted to apply user defined ranges as grouping ranges and tag said data attribute as a grouping attribute when said user defined ranges are available for said data attribute, and wherein said first processing unit is adapted to determine appropriate grouping ranges based on a distribution of said data attribute.