PREDICTIVE ENGINE FOR MULTISTAGE PATTERN DISCOVERY AND VISUAL ANALYTICS RECOMMENDATIONS
A predictive engine for interpreting data structures that includes an interpreter and visualization generator. The interpreter identifies a relational pattern between target feature variables and other feature variables based on recognizing a variable dependency between the target feature data and the other feature data and generate at least one meta-data feature set and associated result metrics. The visualization generator can recommend at least one visualization based on the at least one meta-data feature set and the associated result metrics. The interpreter includes multiple stages that perform variable selection, interaction detection, and pattern discovery and ranking. The predictive engine also includes a data preparer configured to sort, categorize, and filter the data structures according to at least one of data type, hierarchical data structures, unique values, missing values and date/time data.
1 . A predictive engine for interpreting data structures, the predictive engine comprising:
an interpreter configured to identify a relational pattern between target feature variables and other feature variables based on recognizing a variable dependency between the target feature data and the other feature data and generate at least one meta-data feature set and associated result metrics;
a visualization generator configured to recommend at least one visualization based on the at least one meta-data feature set and the associated result metrics.
2 . The predictive engine of claim 1 wherein the interpreter includes multiple stages for performing variable selection, interaction detection, and pattern discovery and ranking.
3 . The predictive engine of claim 1 wherein the variable dependency is one of a linear, non-linear relationship, and non-random pattern.
4 . The predictive engine of claim 1 further comprising a data preparer configured to sort, categorize, and filter the data structures according to at least one of data type, hierarchical data structures, unique values, missing values and date/time data.
5 . The predictive engine of claim 1 wherein the interpreter is further configured to perform a statistical test to determine whether an interaction effect is significant.
6 . The predictive engine of claim 1 wherein the visualization generator generates at least one or more of a multivariate chart and bivariate chart.
7 . The predictive engine of claim 1 wherein the visualization generator is further configured to apply heuristic based rules to recommend the at least one visualization.
8 . A method for operating a predictive engine to interpret data structures, the method comprising:
identifying a relational pattern between target feature data and other feature data based on recognizing a variable dependency between the target feature data and the other feature data;
generating at least one meta-data feature set and associated result metrics; and
recommending at least one visualization based on the at least one meta-data feature set and the associated result metrics.
9 . The method of claim 8 wherein the step of identifying and generating is performed at a first, second, and third stage or more stages wherein variable selection, interaction detection, and pattern discovery and ranking are performed.
10 . The method of claim 8 wherein the variable dependency is one of a linear or non-linear relationship or any non-random pattern.
11 . The method of claim 8 further comprising: sorting, categorizing, and filtering the data structures according to at least one of data type, hierarchical data structures, unique values, missing values and date/time data.
12 . The method of claim 8 further comprising performing a statistical test to determine whether an interaction effect is significant.
13 . The method of claim 1 further comprises generating at least one multivariate chart and bivariate chart.
14 . A non-transitory computer readable storage medium comprising a set of computer instructions executable by a processor for operating a predictive engine to interpret data structures, the computer instructions configured to:
identify a relational pattern between target feature data and other feature data based on recognizing a variable dependency between the target feature data and the other feature data;
generate at least one meta-data feature set and associated result metrics; and
recommend at least one visualization based on the at least one meta-data feature set and the associated result metrics.
15 . The non-transitory computer readable storage medium as recited in claim 14 further including computer instructions configured to identify and generate the relational pattern and at least one meta-data feature set and associated result metrics at a first, second, and third stage or more stages wherein variable selection, interaction detection, and pattern discovery and ranking are performed.
16 . The non-transitory computer readable storage medium as recited in claim 14 wherein the variable dependency is one of a linear and non-linear relationship.
17 . The non-transitory computer readable storage medium as recited in claim 14 further including computer instructions configured to sort, categorize, and filter the data structures according to at least one of data type, hierarchical data structures, unique values, missing values and date/time data.
18 . The non-transitory computer readable storage medium as recited in claim 14 further including computer instructions configured to perform a statistical test to determine whether an interaction effect is significant.
19 . The non-transitory computer readable storage medium as recited in claim 14 further including computer instructions configured to generate at least one of a multivariate chart and a bivariate chart.
20 . The non-transitory computer readable storage medium as recited in claim 14 further including computer instructions configured to apply heuristic based rules to recommend the at least one visualization.