Apparatus, systems and methods for dynamic on-demand context sensitive cluster analysis
Embodiments of apparatus, systems and methods facilitate performing on-demand cluster analysis on a multi-dimensional data set comprising a plurality of dimension members. In some embodiments, the method comprises: selecting a subset of the plurality of dimension members based on a current context associated with the multi dimensional data set; computing a plurality of dimensional scores for a plurality of dimension members in the selected subset, wherein each dimensional score is associated with a distinct dimension member and measures the dimensional influence of the associated dimension member on a metric associated with the multi-dimensional data set; and ranking the plurality of dimension members in the selected subset based on the dimensional score.
1. A processor-implemented method comprising:
selecting a subset of a plurality of dimension members of a multi-dimensional data set, wherein the selected subset of the plurality of dimension members is dynamically determined based on a context associated with the multi-dimensional data set;
computing a plurality of dimensional scores for the dimension members in the selected subset, wherein each dimensional score is associated with a particular dimension member in the subset and is a measure of a dimensional influence of the associated dimension member on a metric associated with the multi-dimensional data set, wherein a dimension member with greater dimensional influence affects a value of the metric over a population more than a dimension member with less dimensional influence; and
ranking the plurality of dimension members in the selected subset based on the dimensional scores.
2. The processor-implemented method of claim 1 , wherein the method is performed on at least one of:
a compute cloud,
a server farm, or
a computing device.
3. The processor-implemented method of claim 1 , wherein the context is dynamically determined based on a portion of the multi-dimensional data set that corresponds to a population being analyzed.
4. The processor-implemented method of claim 1 , further comprising storing a multi-dimensional cube that represents the multi-dimensional data set.
5. The processor-implemented method of claim 4 , further comprising dynamically generating the multi-dimensional cube in response to determining that the multi-dimensional cube is not stored in a cache.
6. The processor-implemented method of claim 1 , wherein the dimensional score associated with the particular dimension member is computed as:
( GM−OM )* GP,
wherein:
GP represents a population size of the particular dimension member;
GM represents a value of the metric over GP; and
OM represents a value of the metric over a population of the multi-dimensional data set defined by the context.
7. The processor-implemented method of claim 6 , wherein the dimensional score associated with the particular dimension member is further computed as, when OP is greater than zero:
( GM−OM )*( GP/OP ),
wherein OP represents a population size over the population of the multi-dimensional data set defined by the context.
8. The processor-implemented method of claim 1 , further comprising displaying a subset of the ranked dimensional members in order of rank.
9. The processor-implemented method of claim 8 , further comprising applying a rank diversification algorithm to increase a number of displayed ranked dimensional members.
10. The processor-implemented method of claim 1 , wherein the computing of the dimensional scores for the dimension members in the selected subset is performed in parallel.
11. A system comprising:
a storage device storing a multi-dimensional database, wherein the multi-dimensional database comprises a plurality of dimension members of a multi-dimensional data set; and
a network-accessible computing device configured to perform operations comprising:
selecting a subset of the plurality of dimension members, wherein the selected subset of the plurality of dimension members is dynamically determined based on a context associated with the multi-dimensional data set;
computing a plurality of dimensional scores for the dimension members in the selected subset, wherein each dimensional score is associated with a particular dimension member in the subset and is a measure of a dimensional influence of the associated dimension member on a metric associated with the multi-dimensional data set, wherein the dimensional score associated with a particular dimension member is computed as a function of a population size of the particular dimension member, a value of the metric over the population size, and a value of the metric over a population of the multi-dimensional data set defined by the context; and
ranking the plurality of dimension members in the selected subset based on the dimensional scores.
12. The system of claim 11 , wherein the computing device is associated with a cloud or a server farm.
13. The system of claim 11 , wherein the operations further comprise sending a subset of the ranked dimensional members to a display device.
14. The system of claim 13 , wherein the display device displays the subset of ranked dimensional members in order of rank and wherein the display device is associated with at least one of:
a smart phone,
a handheld computing device,
a tablet computer,
a notebook computer, or
a desktop computer.
15. The system of claim 11 , wherein the selecting, the computing, and the ranking are performed during execution of a workflow comprising a collection of application-specific functions related to specific business applications.
16. The system of claim 13 , wherein the operations further comprise applying a rank diversification algorithm to increase a number of displayed ranked dimensional members.
17. The system of claim 14 , further comprising a display device wirelessly coupled to the computing device.
18. The system of claim 11 , wherein the context is dynamically determined based on a portion of the multi-dimensional data set that corresponds to a population being analyzed.
19. The system of claim 11 , wherein the dimensional score associated with the particular dimension member is computed as:
( GM−OM )* GP,
wherein:
GP represents the population size of the particular dimension member;
GM represents the value of the metric over GP; and
OM represents the value of the metric over the population of the multi-dimensional data set defined by the context.
20. A non-transitory computer-readable medium comprising instructions that, when executed, cause a processor to perform operations comprising:
selecting a subset of a plurality of dimension members of a multi-dimensional data set, wherein the selected subset of the plurality of dimension members is dynamically determined based on a context associated with the multi-dimensional data set;
computing a plurality of dimensional scores for the dimension members in the selected subset, wherein each dimensional score is associated with a particular dimension member in the subset and is a measure of a dimensional influence of the associated dimension member on a metric associated with the multi-dimensional data set, wherein the dimensional score associated with the particular dimension member is computed as (GM−OM)*GP, wherein GP represents a population size of the dimension member, GM represents a value of the metric over GP, and OM represents a value of the metric over a population of the multi-dimensional data set defined by the context; and
ranking the plurality of dimension members in the selected subset based on the dimensional score.