Model based optimization with focus regions

• US 7,480,663 B2
• Filed: 06/22/2004
• Issued: 01/20/2009
• Est. Priority Date: 06/22/2004
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Colossi, Nathan Gevaerd, DeKimpe, Daniel Martin
• Primary Examiner(s): Pham; Khanh B.
• Assistant Examiner(s): Tang; Jieying
• Application Number:
  US10/874,398
• Publication Number:
  US 20050283488A1
• Time in Patent Office: 1,673 Days
• Field of Search: 707/103, 707/3, 707/100
• US Class Current: 1/1
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP

Abstract

Various embodiments of a method, system and computer program product for optimization of a multidimensional model in a model based performance advisor are disclosed. The multidimensional model comprises groups. Each group has one or more levels. One or more recommended slices associated with the groups are determined based on a focus region.

Claims

1. A computer-implemented method of optimization of a multidimensional model in a model based performance advisor, the multidimensional model comprising a cube comprising a plurality of groups, each group of said plurality of groups having one or more levels, a first group of said plurality of groups having a first plurality of levels, and a second group of said plurality of groups having a second plurality of levels, comprising:

receiving a focus region request specifying a focus region, said focus region being specified by a user, said focus region being associated with said multidimensional model, said focus region request specifying a first query type associated with said focus region, said focus region request specifying said cube, said focus region being associated with said cube, said focus region request specifying a first particular level for said first group of said plurality of groups of said focus region, said focus region request specifying an any level for said second group of said plurality of groups of said focus region;

storing said focus region associated with said first query type and said cube;

receiving, from a user, a recommendation request specifying said cube, wherein said recommendation request is different from said focus region request;

in response to said recommendation request;

retrieving said focus region and said first query type based on said cube specified in said recommendation request;

evaluating, by said model based performance advisor, a plurality of candidate slices based on said focus region and said first query type that are retrieved based on said cube specified in said recommendation request, wherein said each candidate slice of said plurality of candidate slices comprises said first particular level of said first group and one level of said second plurality of levels of said second group, wherein said each candidate slice comprises one level of said each group of said plurality of groups, wherein said each candidate slice comprises a different combination of levels from other candidate slices of said plurality of candidate slices;

selecting a recommended slice from said plurality of candidate slices based on said evaluating, wherein said recommended slice comprises said first particular level of said first group of said focus region; and

generating a query to create said recommended slice.

2. The method of claim 1 wherein a third group of said plurality of groups comprises a third plurality of levels, said focus region of said focus region request also specifies a range for said third group of said plurality of groups of said focus region, said range being specified by a first boundary level of said third plurality of levels of said third group, and a second boundary level of said third plurality of levels of said third group, wherein said focus region comprises a contiguous range of levels bounded by said first boundary level and said second boundary level for said third group, said range comprising less than every level of said third plurality of levels of said third group,

wherein said evaluating said plurality of candidate slices, in response to said range being specified for said third group, said each candidate slice also comprises one level of said range of said third plurality of levels for said third group, wherein levels of said third group outside of said range are not included in said each candidate slice.

3. The method of claim 1 wherein said recommendation request also specifies an amount of disk space, wherein said evaluating said plurality of candidate slices comprises:

determining a rating for said each candidate slice, said rating being based on a percent of slice combinations covered by said each candidate slice, a percent of disk space available to store aggregations associated with said each candidate slice based on specified amount of disk space of said recommendation request, a width of a summary table to store said aggregations of said each candidate slice, whether any columns of said summary table are nullable, and whether any columns of said summary table are associated with a time group of said plurality of groups,

wherein said selecting said recommended slice is also based on said rating for said each candidate slice.

4. The method of claim 1, wherein said focus region request also specifies at least one measure associated with said focus region,

wherein said evaluating evaluates said candidate slices based on said at least one measure associated with said focus region, and does not evaluate said candidate slices based on any measures not associated with said focus region.

5. The method of claim 1 further comprising:

receiving another focus region request specifying an exclude region, said exclude region being associated with said multidimensional model, said another focus region request specifying a second query type associated with said exclude region, said focus region request specifying said cube, said exclude region being associated with said cube, said exclude region of said another focus region request specifying at least one exclude level for said each group of said plurality of groups;

storing said exclude region associated with said second query type and said cube;

wherein, in response to said recommendation request, said exclude region and said second query type are retrieved based on said cube specified in said recommendation request;

evaluating, by said model based performance advisor, another plurality of candidate slices of said cube based on said exclude region and said second query type that are retrieved, wherein no candidate slice of said another plurality of candidate slices comprises said at least one exclude level of said each group;

selecting another recommended slice from said another plurality of candidate slices of said cube based on said evaluating, said another recommended slice not comprising said at least one exclude level of said each group; and

generating another query to create another recommended slice.

6. The method of claim 5 wherein said exclude region is determined based on whether aggregations of a particular slice have been accessed.

7. A system for optimizing a multidimensional model in a model based performance advisor, the multidimensional model comprising a cube comprising a plurality of groups, each group of said plurality of groups having one or more levels, a first group of said plurality of groups having a first plurality of levels, and a second group of said plurality of groups having a second plurality of levels, comprising:

a processor; and

a memory storing a plurality of instructions executable by said processor, said plurality of instructions comprising instructions for;

receiving a focus region request specifying a focus region, said focus region being specified by a user, said focus region being associated with said multidimensional model, said focus region request specifying a first query type associated with said focus region, said focus region request specifying said cube, said focus region being associated with said cube, said focus region request specifying a first particular level for said first group of said plurality of groups of said focus region, said focus region request specifying an any level for said second group of said plurality of groups of said focus region;

storing said focus region associated with said first query type and said cube;

receiving, from a user, a recommendation request specifying said cube, wherein said recommendation request is different from said focus region request;

in response to said recommendation request;

retrieving said focus region and said first query type based on said cube specified in said recommendation request;

evaluating, by said model based performance advisor, a plurality of candidate slices based on said focus region and said first query type that are retrieved based on said cube specified in said recommendation request, wherein said each candidate slice of said plurality of candidate slices comprises said first particular level of said first group and one level of said second plurality of levels of said second group, wherein said each candidate slice comprises one level of said each group of said plurality of groups, wherein said each candidate slice comprises a different combination of levels from other candidate slices of said plurality of candidate slices;

selecting a recommended slice from said plurality of candidate slices based on said evaluating, wherein said recommended slice comprises said first particular level of said first group of said focus region; and

generating a query to create said recommended slice.

8. The system of claim 7 wherein a third group of said plurality of groups comprises a third plurality of levels, said focus region of said focus region request also specifies a range for said third group of said plurality of groups of said focus region, said range being specified by a first boundary level of said third plurality of levels of said third group, and a second boundary level of said third plurality of levels of said third group, wherein said focus region comprises a contiguous range of levels bounded by said first boundary level and said second boundary level for said third group, said range comprising less than every level of said third plurality of levels of said third group,

wherein said evaluating said plurality of candidate slices, in response to said range being specified for said third group, said each candidate slice also comprises one level of said range of said third plurality of levels for said third group, wherein levels of said third group outside of said range are not included in said each candidate slice.

9. The system of claim 7 wherein said recommendation request also specifies an amount of disk space, wherein said evaluating said plurality of candidate slices comprises:

determining a rating for said each candidate slice, said rating being based on a percent of slice combinations covered by said each candidate slice, a percent of disk space available to store aggregations associated with said each candidate slice based on specified amount of disk space of said recommendation request, a width of a summary table to store said aggregations of said each candidate slice, whether any columns of said summary table are nullable, and whether any columns of said summary table are associated with a time group of said plurality of groups,

wherein said selecting said recommended slice is also based on said rating for said each candidate slice.

10. The system of claim 7, wherein said focus region request also specifies at least one measure associated with said focus region,

wherein said evaluating evaluates said candidate slices based on said at least one measure associated with said focus region, and does not evaluate said candidate slices based on any measures not associated with said focus region.

11. The system of claim 7 said memory comprising instructions for:

receiving another focus region request specifying an exclude region, said exclude region being associated with said multidimensional model, said another focus region request specifying a second query type associated with said exclude region, said focus region request specifying said cube, said exclude region being associated with said cube, said exclude region of said another focus region request specifying at least one exclude level for said each group of said plurality of groups;

storing said exclude region associated with said second query type and said cube;

wherein, in response to said recommendation request, said exclude region and said second query type are retrieved based on said cube specified in said recommendation request;

evaluating, by said model based performance advisor, another plurality of candidate slices of said cube based on said exclude region and said second query type that are retrieved, wherein no candidate slice of said another plurality of candidate slices comprises said at least one exclude level of said each group;

selecting another recommended slice from said another plurality of candidate slices of said cube based on said evaluating, said another recommended slice not comprising said at least one exclude level of said each group; and

generating another query to create another recommended slice.

12. The system of claim 11 wherein said exclude region is determined based on whether aggregations of a particular slice have been accessed.

13. A computer program product comprising a computer-readable medium, said computer program product for optimizing a multidimensional model in a model based performance advisor, the multidimensional model comprising a cube comprising a plurality of groups, each group of said plurality of groups having one or more levels, a first group of said plurality of groups having a first plurality of levels, and a second group of said plurality of groups having a second plurality of levels, said computer program product comprising:

first program instructions to receive a focus region request specifying a focus region, said focus region being specified by a user, said focus region being associated with said multidimensional model, said focus region request specifying a first query type associated with said focus region, said focus region request specifying said cube, said focus region being associated with said cube, said focus region request specifying a first particular level for said first group of said plurality of groups of said focus region, said focus region request specifying an any level for said second group of said plurality of groups of said focus region;

second program instructions to store said focus region associated with said first query type and said cube;

third program instructions to receive, from a user, a recommendation request specifying said cube, wherein said recommendation request is different from said focus region request;

fourth program instructions to, in response to said recommendation request;

retrieve said focus region and said first query type based on said cube specified in said recommendation request;

evaluate, by said model based performance advisor, a plurality of candidate slices based on said focus region and said first query type that are retrieved based on said cube specified in said recommendation request, wherein said each candidate slice of said plurality of candidate slices comprises said first particular level of said first group and one level of said second plurality of levels of said second group, wherein said each candidate slice comprises one level of said each group of said plurality of groups, wherein said each candidate slice comprises a different combination of levels from other candidate slices of said plurality of candidate slices;

select a recommended slice from said plurality of candidate slices based on said evaluating, wherein said recommended slice comprises said first particular level of said first group of said focus region; and

generate a query to create said recommended slice;

wherein said first, second, third and fourth program instructions are stored on said computer-readable medium.

14. The computer program product of claim 13 wherein a third group of said plurality of groups comprises a third plurality of levels, said focus region of said focus region request also specifies a range for said third group of said plurality of groups of said focus region, said range being specified by a first boundary level of said third plurality of levels of said third group, and a second boundary level of said third plurality of levels of said third group, wherein said focus region comprises a contiguous range of levels bounded by said first boundary level and said second boundary level for said third group, said range comprising less than every level of said third plurality of levels of said third group,

wherein said evaluate said plurality of candidate slices, in response to said range being specified for said third group, said each candidate slice also comprises one level of said range of said third plurality of levels for said third group, wherein levels of said third group outside of said range are not included in said each candidate slice.

15. The computer program product of claim 13 wherein said recommendation request also specifies an amount of disk space, wherein said evaluate said plurality of candidate slices comprises:

determine a rating for said each candidate slice, said rating being based on a percent of slice combinations covered by said each candidate slice, a percent of disk space available to store aggregations associated with said each candidate slice based on specified amount of disk space of said recommendation request, a width of a summary table to store said aggregations of said each candidate slice, whether any columns of said summary table are nullable, and whether any columns of said summary table are associated with a time group of said plurality of groups,

wherein said select said recommended slice is also based on said rating for said each candidate slice.

16. The computer program product of claim 13, wherein said focus region request also specifies at least one measure associated with said focus region,

wherein said evaluate evaluates said candidate slices based on said at least one measure associated with said focus region, and does not evaluate said candidate slices based on any measures not associated with said focus region.

17. The computer program product of claim 13 further comprising:

fifth program instructions to receive another focus region request specifying an exclude region, said exclude region being associated with said multidimensional model, said another focus region request specifying a second query type associated with said exclude region, said focus region request specifying said cube, said exclude region being associated with said cube, said exclude region of said another focus region request specifying at least one exclude level for said each group of said plurality of groups;

sixth program instructions to store said exclude region associated with said second query type and said cube;

wherein said fourth program instructions, in response to said recommendation request, said exclude region and said second query type are retrieved based on said cube specified in said recommendation request;

wherein said fourth program instructions evaluate, by said model based performance advisor, another plurality of candidate slices of said cube based on said exclude region and said second query type that are retrieved, wherein no candidate slice of said another plurality of candidate slices comprises said at least one exclude level of said each group;

wherein said fourth program instructions select another recommended slice from said another plurality of candidate slices of said cube based on said evaluating, said another recommended slice not comprising said at least one exclude level of said each group; and

wherein said fourth program instructions generate another query to create another recommended slice;

wherein said fifth program instructions and said sixth program instructions are stored on said computer-readable medium.

18. The computer program product of claim 17 wherein said exclude region is determined based on whether aggregations of a particular slice have been accessed.