Creating a survey sample group according to a desired participant distribution in real time

1. A method of creating a balanced survey group in real time, the method comprising:

creating, using a processor of a computing system, a vector representation of an entry, the vector representation being a multidimensional vector representation identifying an exclusion or inclusion of a candidate from each of a plurality of categories;

calculating, using the processor of the computing system, a similarity value indicating a similarity between the vector representation and a first histogram vector, the first histogram vector representing a dynamically balanced emptiness associated with each of a plurality of categories determined based on a first goal and during a creation of an initial survey group, the similarity value being a result of a dot product between the vector representation and the first histogram vector;

determining, using the processor of the computing system, whether the similarity value is greater than a similarity threshold by comparing the result of the dot product with the similarity threshold, the similarity threshold being determined based on a linear superposition of a most-preferred entry vector and a least-preferred entry vector representing a type of respondent most needed and least needed; and

integrating, using the processor of the computing system, the entry vector representation into the first histogram vector, wherein the first histogram relates to the first goal, and wherein the integrating enables, at least in part, dynamic balancing of the first histogram vector while the initial survey group is being created.

2. A method as recited in claim 1 wherein creating a vector representation of an entry further comprises:

processing entry data, wherein an entry vector has a plurality of elements.

3. A method as recited in claim 1 further comprising:

deriving a most-preferred entry vector by examining the first histogram.

4. A method as recited in claim 3 further comprising:

deriving a least-preferred entry vector by examining the first histogram.

5. A method as recited in claim 4 further comprising:

calculating the similarity threshold by using the most-preferred entry vector and the least-preferred entry vector and a manually-adjusted variable.

6. A method as recited in claim 5 wherein the manually-adjusted variable corresponds to a filter strength used to balance a trade-off between survey histogram balance and time taken to create the survey group.

7. A method as recited in claim 5 wherein adjusting the variable determines the accuracy of a final histogram and the speed at which the final histogram is created.

8. A method as recited in claim 7 further comprising:

updating the most-preferred entry vector and least-preferred entry vector with each iteration.

9. A method as recited in claim 1 further comprising:

modifying the first survey histogram based on an integrated entry vector representation.

10. A method as recited in claim 1 wherein an entry vector has eight elements including two for gender, three for income, and three for age.

11. A method as recited in claim 1 further comprising:

integrating the first goal with a second goal and a third goal, wherein the first goal is having a minimum number of entries in a survey, the second goal is having at least one of each possible entry in the survey, the third goal is having at least one of each possible pairs in the survey.

12. A method as recited in claim 1 further comprising:

checking if the first goal has been met.

13. A method as recited in claim 1 further comprising:

rejecting the entry if the similarity value does not exceed the similarity threshold, wherein the first histogram is updated with the entry.

14. A method of creating a balanced survey group in real time with multiple goals, the method comprising:

creating a vector representation of an entry, the vector representation being a multidimensional vector representation identifying an exclusion or inclusion of a candidate from each of a plurality of categories;

calculating a first similarity value indicating a similarity between the vector representation and a first histogram vector, the first histogram vector representing a dynamically balanced emptiness associated with each of a plurality of categories determined based on a first goal and during a creation of an initial survey group, the similarity value being a result of a dot product between the vector representation and the first histogram vector;

calculating a second similarity value indicating a similarity between the vector representation and a second histogram vector;

calculating a third similarity value indicating a similarity between the vector representation and a third histogram vector;

integrating the first, second, and third similarity values to calculate a final similarity value; and

integrating the entry vector representation into the first histogram vector, the second histogram vector, and the third histogram vector based on a comparison of the first similarity value, the second similarity value, and the third similarity value with at least one similarity threshold, wherein the first histogram relates to the first goal, the second histogram relates to a second goal, and the third histogram relates to a third goal, and wherein the integrating enables, at least in part, dynamic balancing of the first histogram vector, the second histogram vector, and the third histogram vector while at least one initial survey group is being created.

15. A method as recited in claim 14 further comprising:

assigning weights to the first similarity value, the second similarity value, and the third similarity value before calculating the final similarity value.

16. A method as recited in claim 14 further comprising:

checking if all of the first goal, the second goal, and the third goal are reached.

17. A method as recited in claim 14 further comprising:

deriving a most-preferred entry and a least-preferred entry for the first histogram;

deriving a most-preferred entry and a least-preferred entry for the second histogram; and

deriving a most-preferred entry and a least-preferred entry for the third histogram.

18. A method as recited in claim 14 further comprising:

creating a three-dimensional vector representation of the entry for use with the third similarity value and the third histogram vector.

19. A computing apparatus for creating a balanced survey group in real time, the apparatus comprising:

means for creating a vector representation of an entry, an entry being a potential respondent in a survey, and the vector representation being a multidimensional vector representation identifying an exclusion or inclusion of the potential respondent from each of a plurality of categories;

means for calculating a similarity value indicating a similarity between the entry vector representation and a first histogram vector, the first histogram vector representing a dynamically balanced emptiness associated with each of a plurality of categories determined based on a first goal and during a creation of an initial survey group, the similarity value being a result of a dot product between the vector representation and the first histogram vector;

means for determining whether the similarity value is greater than a similarity threshold by comparing the result of the dot product with the similarity threshold, the similarity threshold being determined based on a linear superposition of a most-preferred entry vector and a least-preferred entry vector representing a type of respondent most needed and least needed; and

integrating the entry vector representation into the first histogram vector in real time, wherein the first histogram relates to the first goal, and wherein the integrating enables, at least in part, dynamic balancing of the first histogram vector while the initial survey group is being created.