Abstract: Embodiments of the invention provide a technical solution by generating a value index score based on aggregation of a value from a combination of features as a unit. In one embodiment, instead of generating a value index score based on a collection of features with each feature being a discrete parameter, aspects of the invention generate the value index score while accounting for weights of a combination of features as a unit. Furthermore, embodiments of the invention generate a weight value for each feature and that the weight, not only will it be a factor in the calculation, but also be modifiable in response to other factors of the features.

Abstract: Embodiments of the invention improve over conventional or routine technologies by generating a separate data structure or organization, other than those temporary search files that are typically used, that facilitates the processing of the datasets. In one embodiment, instead of using sorted temp files or running sorting algorithms, a meta-join and/or meta-group-by indexes may be generated or created that provide a quick access to the records in the datasets. Moreover, embodiments of the invention eliminate the need to sort or pre-sort datasets before or during a query is conducted on the dataset. Aspects of the invention use, for example, the meta-join and/or meta-group-by indexes to pre-organize the data such that, when executing a query against the dataset, the query is executed against the meta-join and/or meta-group-by indexes.

Abstract: A graphical user interface showing relevant sliding scale cluster groups having a processor generating a ranking of Top N1 features of the seed dataset in order of frequency of their occurrence. The processor generates another ranking of Top N2 features of the seed dataset in order of frequency of their occurrence in correlation with their equivalent percentage in total dataset. The Top M co-related features is identified for each of the Top N (N1+N2) features that are present for the seed dataset. The processor, for each one in M×N set, generates a sliding scale cluster via permutation of Top M features. The processor sorts each permutation based on closest occurrence match. The Seed Group Meta Bitmap Index is generated for the seed audience segment. For each cluster, the processor calculates the available amplification count from the Total Audience Bitmap Index, until the desired amplification is achieved.

Abstract: A graphical user interface showing relevant sliding scale cluster groups having a processor generating a ranking of Top N1 features of the seed dataset in order of frequency of their occurrence. The processor generates another ranking of Top N2 features of the seed dataset in order of frequency of their occurrence in correlation with their equivalent percentage in total dataset. The Top M co-related features is identified for each of the Top N (N1+N2) features that are present for the seed dataset. The processor, for each one in M×N set, generates a sliding scale cluster via permutation of Top M features. The processor sorts each permutation based on closest occurrence match. The Seed Group Meta Bitmap Index is generated for the seed audience segment. For each cluster, the processor calculates the available amplification count from the Total Audience Bitmap Index, until the desired amplification is achieved.

Abstract: Embodiments of the invention improve over conventional or routine technologies by generating a separate data structure or organization, other than those temporary search files that are typically used, that facilitates the processing of the datasets. In one embodiment, instead of using sorted temp files or running sorting algorithms, a meta-join and/or meta-group-by indexes may be generated or created that provide a quick access to the records in the datasets. Moreover, embodiments of the invention eliminate the need to sort or pre-sort datasets before or during a query is conducted on the dataset. Aspects of the invention use, for example, the meta-join and/or meta-group-by indexes to pre-organize the data such that, when executing a query against the dataset, the query is executed against the meta-join and/or meta-group-by indexes.