ESTIMATING A COMPUTING JOB COMPLEXITY

• US 20100257116A1
• Filed: 04/05/2010
• Published: 10/07/2010
• Est. Priority Date: 04/06/2009
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services GmbH (Accenture PLC)
• Inventors: Chauhan, Satyendra, Tayal, Anu, Agarwal, Shalini, Ray, Santosh, Sahasrabudhe, Sudha, Tiyagi, Vineet, Basha, Kalamalla, Mehta, Dharmesh Rajendra, Kohli, Shagufta Shashank
• Granted Patent: US 9,563,866 B2
• Time in Patent Office: Days
• US Class Current: 705/348
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06313 Resource planning in a project environment
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling

Abstract

In a method for estimating a complexity of a computing job, selected data objects relevant to a data repository are retrieved. In addition, points are assigned to multiple elements of the selected data objects according to a predefined schedule and scores for the selected data objects are calculated by applying a mathematical function to the multiple elements and complexities of the data objects are estimated based upon the calculated scores and the predefined schedule. In addition, a complexity of the computing job is estimated based upon the estimated complexities of the data objects and the estimated complexity of the computing job is stored.

Claims

1. A computer readable storage medium on which is embedded one or more computer programs, said one or more computer programs implementing a method for estimating a complexity of a computing job, said one or more computer programs comprising computer readable code for:

retrieving selected data objects relevant to a data repository, wherein each of the selected data objects is formed of multiple elements;

assigning points to the multiple elements according to a predefined schedule;

calculating scores for the selected data objects by applying a mathematical function to the multiple elements;

estimating complexities of the data objects based upon the calculated scores and the predefined schedule; and

estimating a complexity of the computing job based upon the estimated complexities of the data objects.

2. The computer readable storage medium according to claim 1, said one or more computer programs further comprising computer readable code for:

receiving user input pertaining to a historical knowledge of the data objects; and

creating the predefined schedule of points for the multiple elements, wherein the predefined schedule of points is developed from a historical knowledge of the data objects.

3. The computer readable storage medium according to claim 1, said one or more computer programs further comprising computer readable code for

estimating an amount of resources required to perform the computing job based upon the complexity of the computing job.

4. The computer readable storage medium according to claim 3, said one or more computer programs further comprising computer readable code for

employing the estimated amount of resources in developing a bid for performing the computing job.

5. The computer readable storage medium according to claim 1, said one or more computer programs further comprising computer readable code for

outputting a summary sheet of the estimated computing job complexity that depicts the estimated complexities of each of the data objects.

6. The computer readable storage medium according to claim 1, wherein the selected data objects comprise at least one of mappings and graphs, wherein the multiple elements of at least one of the mappings and graphs comprise transformations and SQL overrides, and wherein calculating scores of the data objects further comprises calculating transformation scores for each of at least one of the mappings and graphs by multiplying the transformations by their respective points to obtain respective products, and summing the products.

7. The computer readable storage medium according to claim 6, wherein calculating scores of the data objects further comprises calculating SQL override scores for each of at least one of the mappings and graphs by determining points assigned to each of the SQL overrides based upon lengths of the SQL overrides, wherein the mapping scores comprise sums of respective transformation scores and SQL override scores.

8. The computer readable storage medium according to claim 7, wherein estimating complexities of the data objects further comprises estimating complexities of at least one of the mappings and graphs based upon the scores of each of at least one of the mappings and graphs and wherein estimating a complexity of the computing job further comprises estimating the complexity of the computing job based upon a function of the estimated complexities of each of at least one of the mappings and graphs.

9. The computer readable storage medium according to claim 8, wherein estimating complexities of at least one of the mappings and graphs further comprises estimating the complexities of at least one of the mappings and graphs based upon where the scores for each of at least one of the mappings and graphs falls in a complexity schedule that relates ranges of scores to complexity values.

10. The computer readable storage medium according to claim 6, said one or more computer programs further comprising computer readable code for:

accessing an intelligence report;

identifying relevant data from the intelligence report, wherein the relevant data comprises the transformations and the SQL overrides of the mappings; and

creating a spreadsheet with information pertaining to the relevant data, said spreadsheet facilitating retrieval of the selected data objects.

11. The computer readable storage medium according to claim 10, wherein identifying the relevant data further comprises identifying a summary count of the transformations and lengths of the SQL overrides, and wherein creating the spreadsheet further comprises creating the spreadsheet to include the summary count of the transformations and the lengths of the SQL overrides.

12. The computer readable storage medium according to claim 1, wherein the selected data objects comprise business objects universes and business objects reports, and wherein the multiple elements comprise universe components of the business objects universes and report components of the business objects reports.

13. The computer readable storage medium according to claim 12, wherein calculating scores for the data objects further comprises calculating scores for each of the universe components by determining the numbers of each of the respective universe components, and wherein estimating complexities of the business objects universes further comprises:

comparing the number of universe components to the predefined schedule to determine complexities of the respective universe components, wherein the predefined schedule includes a correlation between the numbers of respective universe components and a plurality of complexity designations;

multiplying the points assigned to each of the universe components by their respective complexity designations, wherein the predefined schedule further includes a correlation between the respective universe components and the points assigned to the respective universe components; and

wherein estimating complexities of the data objects further comprises estimating the complexities of the business objects universes based upon the complexity designations of the respective universe components and the points assigned to the respective universe components.

14. The computer readable storage medium according to claim 12, wherein calculating scores for the data objects further comprises calculating scores for each of the report components by determining the numbers of each of the is respective report components, and wherein estimating complexities of the business objects universes further comprises:

comparing the number of report components to the predefined schedule to determine complexities of the respective report components, wherein the predefined schedule includes a correlation between the numbers of respective report components and a plurality of complexity designations;

multiplying the points assigned to each of the report components by their respective complexity designations, wherein the predefined schedule further includes a correlation between the respective report components and the points assigned to the respective report components; and

wherein estimating complexities of the data objects further comprises estimating the complexities of the business objects universes based upon the complexity designations of the respective report components and the points assigned to the respective report components.

15. The computer readable storage medium according to claim 12, said one or more computer programs further comprising computer readable code for:

accessing an intelligence report;

identifying relevant data from the intelligence report, wherein the relevant data comprises the universe components of the business objects universes and the report components of the report universes; and

creating a spreadsheet with information pertaining to the relevant data.

16. The computer readable storage medium according to claim 15, wherein identifying the relevant data further comprises identifying a summary count of the universe components and the report components and wherein creating the spreadsheet further comprises creating the spreadsheet to include the summary count of the universe components and the report components.

17. A processing apparatus for estimating a complexity of a computing job, said processing apparatus comprising:

at least one processor;

a storage device in communication with the at least one processor, said storage device storing instructions that, when executed by the at least one processor, cause the at least one processor to;

retrieve selected data objects relevant to a data repository, wherein each of the selected data objects is formed of multiple elements;

assign points to the multiple elements according to a predefined schedule;

calculate scores for the selected data objects by applying a mathematical function to the multiple elements;

estimate complexities of the data objects based upon the calculated scores and the predefined schedule; and

estimate a complexity of the computing job based upon the calculated scores.

18. The processing apparatus according to claim 17, said storage device further storing instructions that, when executed by the at least one processor, cause the at least one processor to:

create the predefined schedule of points for the multiple elements from a user'"'"'s historical knowledge of the data objects.

19. The processing apparatus according to claim 17, said storage device further storing instructions that, when executed by the at least one processor, cause the at least one processor to:

estimate an amount of resources required to perform the computing job based upon the complexity of the computing job.

20. The processing apparatus according to claim 17, wherein the selected data objects comprise at least one of mappings and graphs, wherein the multiple elements of at least one of the mappings and graphs comprise transformations and SQL overrides, said storage device further storing instructions that, when executed by the at least one processor, cause the at least one processor to:

calculate transformation scores for each of at least one of the mappings and graphs by multiplying the transformations by their respective points to obtain respective products, and summing the products.

21. The processing apparatus according to claim 17, said storage device further storing instructions that, when executed by the at least one processor, cause the at least one processor to:

calculate SQL override scores for each of at least one of the mappings and graphs by determining points assigned to each of the SQL overrides based upon lengths of the SQL overrides, wherein at least one of the mapping scores and graphs scores comprise sums of respective transformation scores and SQL override scores.

22. The processing apparatus according to claim 17, said storage device further storing instructions that, when executed by the at least one processor, cause the at least one processor to:

estimate complexities of at least one of the mappings and graphs based upon the scores of each of at least one of the mappings and graphs and wherein estimating a complexity of the computing job further comprises estimating the complexity of the computing job based upon a function of the estimated complexities of each of at least one of the mappings and graphs.

23. The processing apparatus according to claim 17, said storage device further storing instructions that, when executed by the at least one processor, cause the at least one processor to:

estimate complexities of at least one of the mappings and graphs based upon the scores of each of at least one of the mappings and graphs and wherein estimating the complexities of at least one of the mappings and graphs based upon where the scores for each of at least one of the mappings and graphs falls in a complexity schedule that relates ranges of scores to complexity values.

24. The processing apparatus according to claim 17, said storage device further storing instructions that, when executed by the at least one processor, cause the at least one processor to:

access an intelligence report;

identify relevant data from the intelligence report, wherein the mappings comprise transformations and SQL overrides and wherein the relevant data comprises the transformations and the SQL overrides of the mappings; and

create a spreadsheet with information pertaining to the relevant data, wherein the spreadsheet facilitates retrieval of the selected data objects.

25. The processing apparatus according to claim 17, wherein the selected data objects comprise business objects universes and business objects reports, and wherein the multiple elements comprise universe components of the business objects universes and report components of the business objects reports.

26. The processing apparatus according to claim 25, said storage device further storing instructions that, when executed by the at least one processor, cause the at least one processor to:

calculate scores for each of the universe components by determining the numbers of each of the respective universe components; and

estimate complexities of the business objects universes by,

comparing the number of universe components to the predefined schedule to determine complexities of the respective universe components, wherein the predefined schedule includes a correlation between the numbers of respective universe components and a plurality of complexity designations;

multiplying the points assigned to each of the universe components by their respective complexity designations, wherein the predefined schedule further includes a correlation between the respective universe components and the points assigned to the respective universe components; and

estimating the complexities of the business objects universes based upon the complexity designations of the respective universe components and the points assigned to the respective universe components.

27. The processing apparatus according to claim 25, said storage device further storing instructions that, when executed by the at least one processor, cause the at least one processor to:

calculate scores for each of the report components by determining the numbers of each of the respective report components; and

estimate complexities of the business objects universes by,

comparing the number of report components to the predefined schedule to determine complexities of the respective report components, wherein the predefined schedule includes a correlation between the numbers of respective report components and a plurality of complexity designations;

multiplying the points assigned to each of the report components by their respective complexity designations, wherein the predefined schedule further includes a correlation between the respective report components and the points assigned to the respective report components; and

estimating the complexities of the business objects universes based upon the complexity designations of the respective report components and the points assigned to the respective report components.

28. The processing apparatus according to claim 25, said storage device further storing instructions that, when executed by the at least one processor, cause the at leak one processor to:

access an intelligence report;

identify relevant data from the intelligence report, wherein the relevant data comprises the universe components of the business objects universes and the report components of the report universes; and

create a spreadsheet with information pertaining to the relevant data.

29. The processing apparatus according to claim 28, said storage device further storing instructions that, when executed by the at least one processor, cause the at least one processor to:

identify a summary count of the universe components and the report components and create the spreadsheet to include the summary count of the universe components and the report components.