Dynamic data collection

• US 9,390,157 B2
• Filed: 03/21/2006
• Issued: 07/12/2016
• Est. Priority Date: 03/21/2006
• Status: Expired due to Fees

• Current Assignee: Kyndryl Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Attili, Srinivas, Divekar, Ajay A., Jujjuru, Srinivas, Miglani, Pradeep
• Primary Examiner(s): Shmatov, Alexey
• Application Number:
  US11/385,585
• Publication Number:
  US 20070226236A1
• Time in Patent Office: 3,766 Days
• Field of Search: 707/101, 707/999.101, 707/802, 705/27
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2264 Multidimensional index structures
• CPC Class Codes: G06F 16/2282 Tablespace storage structures; Management thereof
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/972 Access to data in other repository systems, e.g. legacy data or dynamic Web page generation
• CPC Class Codes: G06F 9/451 Execution arrangements for user interfaces

Abstract

A method, system, and computer storage medium dynamically generate a web-based data collection tool. The method comprises defining a web-based framework of data dimensions. The web-based framework is populated by an end-user with measures for data dimension intersections that are selected by the end-user, where a data dimension intersection is a combination of multiple data dimensions. The web-based framework is transmitted to the end-user. The end-user populates the web-based framework on the fly with one or more user-selected data dimensions to create a user-defined data collection tool, and the user-selected data dimensions are chosen from a plurality of data dimensions that are offered through the web-based framework. The web based framework receives the user-defined data collection tool.

Claims

1. A method for dynamically generating a web-based data collection tool, the method comprising:

generating, by a processor of a computer system, a web-based framework, wherein the web-based framework is a table comprising N+1 columns and M+1 rows, wherein the N+1 columns comprises a first column and N remaining columns, wherein the M rows comprises a first row and M remaining rows, wherein the first row identifies N dimensions consisting of N independent variables respectively associated with the N columns, wherein the first column identifies M dimensions consisting of M independent variables respectively associated with the M rows, wherein a two-dimensional array within the table comprises N×

M intersections of the N remaining columns and the M remaining rows, wherein each intersection is characterized by two independent variables consisting of the independent variable of the column and the independent variable of the row defining each intersection, wherein the first row further identifies a filter comprising a list of K dimensions consisting of K dependent variables, and wherein N, M and K are each an integer of at least 2;

said processor transmitting the web-based framework to an end-user;

after transmitting the framework to the end-user, said processor receiving a user-defined data collection tool comprising the table with the N×

M intersections in the table each containing a measure of a dependent variable selected by the end-user independently from the K dependent variables, wherein a measure of a dependent variable is a concrete value of the dependent variable, wherein the dependent variable for each intersection is a function of the two dependent variables characterizing each intersection, and wherein at least one row of intersections in the array and/or at least one column of intersections in the array includes measures of different dependent variables selected by the end-user from the K dependent variables.

2. The method of claim 1, wherein the first column further identifies a fixed dimension consisting of a constraint, and wherein the dependent variable for each intersection is a function of the two dependent variables characterizing each intersection subject to the constraint.

3. The method of claim 2, wherein the fixed dimension appears in the first column in each row of the M rows.

4. The method of claim 1, wherein the N×

M intersections in the data collection tool includes measures of all of the K dependent variables.

5. The method of claim 1, wherein the K dependent variables consist of K independent species of a generic parameter, wherein the first row in the data collection tool includes a measure of a different species of the generic parameter for each of the N independent variables respectively distributed in the N columns, wherein the different species encompasses all of the K independent species, and wherein the different species for each of the N independent variables are with respect to the M independent variables collectively.

6. The method of claim 5, wherein N=2, M=2, and K=3, wherein the generic parameter is cost, wherein the K independent species of the generic parameter of cost are first costs, second costs, and third costs, and wherein the different species of the generic parameter of cost is total cost which encompasses the first costs, the second costs, and the third costs.

7. A computer system, comprising a processor, a memory coupled to the processor, and a computer readable hardware storage device coupled to the processor, said storage device containing program code executable by the processor via the memory to implement a method for dynamically generating a web-based data collection tool, said method comprising:

said processor generating a web-based framework, wherein the web-based framework is a table comprising N+1 columns and M+1 rows, wherein the N+1 columns comprises a first column and N remaining columns, wherein the M rows comprises a first row and M remaining rows, wherein the first row identifies N dimensions consisting of N independent variables respectively associated with the N columns, wherein the first column identifies M dimensions consisting of M independent variables respectively associated with the M rows, wherein a two-dimensional array within the table comprises N×

M intersections of the N remaining columns and the M remaining rows, wherein each intersection is characterized by two independent variables consisting of the independent variable of the column and the independent variable of the row defining each intersection, wherein the first row further identifies a filter comprising a list of K dimensions consisting of K dependent variables, and wherein N, M and K are each an integer of at least 2;

said processor transmitting the web-based framework to an end-user;

after transmitting the framework to the end-user, said processor receiving a user-defined data collection tool comprising the table with the N×

M intersections in the table each containing a measure of a dependent variable selected by the end-user independently from the K dependent variables, wherein a measure of a dependent variable is a concrete value of the dependent variable, wherein the dependent variable for each intersection is a function of the two dependent variables characterizing each intersection, and wherein at least one row of intersections in the array and/or at least one column of intersections in the array includes measures of different dependent variables selected by the end-user from the K dependent variables.

8. The computer system of claim 7, wherein the first column further identifies a fixed dimension consisting of a constraint, and wherein the dependent variable for each intersection is a function of the two dependent variables characterizing each intersection subject to the constraint.

9. The computer system of claim 8, wherein the fixed dimension appears in the first column in each row of the M rows.

10. The computer system of claim 7, wherein the N×

M intersections in the data collection tool includes measures of all of the K dependent variables.

11. The computer system of claim 7, wherein the K dependent variables consist of K independent species of a generic parameter, wherein the first row in the data collection tool includes a measure of a different species of the generic parameter for each of the N independent variables respectively distributed in the N columns, wherein the different species encompasses all of the K independent species, and wherein the different species for each of the N independent variables are with respect to the M independent variables collectively.

12. The computer system of claim 11, wherein N=2, M=2, and K=3, wherein the generic parameter is cost, wherein the K independent species of the generic parameter of cost are first costs, second costs, and third costs, and wherein the different species of the generic parameter of cost is total cost which encompasses the first costs, the second costs, and the third costs.

13. A computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code containing instructions executable by a processor of a computer system to implement a method for dynamically generating a web-based data collection tool, said method comprising:

said processor generating a web-based framework, wherein the web-based framework is a table comprising N+1 columns and M+1 rows, wherein the N+1 columns comprises a first column and N remaining columns, wherein the M rows comprises a first row and M remaining rows, wherein the first row identifies N dimensions consisting of N independent variables respectively associated with the N columns, wherein the first column identifies M dimensions consisting of M independent variables respectively associated with the M rows, wherein a two-dimensional array within the table comprises N×

M intersections of the N remaining columns and the M remaining rows, wherein each intersection is characterized by two independent variables consisting of the independent variable of the column and the independent variable of the row defining each intersection, wherein the first row further identifies a filter comprising a list of K dimensions consisting of K dependent variables, and wherein N, M and K are each an integer of at least 2;

said processor transmitting the web-based framework to an end-user;

after transmitting the framework to the end-user, said processor receiving a user-defined data collection tool comprising the table with the N×

M intersections in the table each containing a measure of a dependent variable selected by the end-user independently from the K dependent variables, wherein a measure of a dependent variable is a concrete value of the dependent variable, wherein the dependent variable for each intersection is a function of the two dependent variables characterizing each intersection, and wherein at least one row of intersections in the array and/or at least one column of intersections in the array includes measures of different dependent variables selected by the end-user from the K dependent variables.

14. The computer program product of claim 13, wherein the first column further identifies a fixed dimension consisting of a constraint, and wherein the dependent variable for each intersection is a function of the two dependent variables characterizing each intersection subject to the constraint.

15. The computer program product of claim 14, wherein the fixed dimension appears in the first column in each row of the M rows.

16. The computer program product of claim 13, wherein the N×

M intersections in the data collection tool includes measures of all of the K dependent variables.

17. The computer program product of claim 13, wherein the K dependent variables consist of K independent species of a generic parameter, wherein the first row in the data collection tool includes a measure of a different species of the generic parameter for each of the N independent variables respectively distributed in the N columns, wherein the different species encompasses all of the K independent species, and wherein the different species for each of the N independent variables are with respect to the M independent variables collectively.

18. The computer program product of claim 17, wherein N=2, M=2, and K=3, wherein the generic parameter is cost, wherein the K independent species of the generic parameter of cost are first costs, second costs, and third costs, and wherein the different species of the generic parameter of cost is total cost which encompasses the first costs, the second costs, and the third costs.