Abstract: Provided is a system and method for automated feature engineering of multidimensional data. The system and method may generate a flat data set for use in training a machine learning model from a multidimensional data set. In one example, the method may include extracting metadata of multidimensional data, generating declarative statements of features that are to be derived from the multidimensional data based on hierarchical attributes of the multidimensional data which are identified from the extracted metadata, querying, via a query engine, the multidimensional data based on the generated declarative statements to generate the derived features, and storing the derived features in a training data set.

Abstract: Provided is a system and method for automated feature engineering of multidimensional data. The system and method may generate a flat data set for use in training a machine learning model from a multidimensional data set. In one example, the method may include extracting metadata of multidimensional data, generating declarative statements of features that are to be derived from the multidimensional data based on hierarchical attributes of the multidimensional data which are identified from the extracted metadata, querying, via a query engine, the multidimensional data based on the generated declarative statements to generate the derived features, and storing the derived features in a training data set.

Abstract: Provided is a system and method for combining feature sets of data via single vertical union operation. In one example, the method may include receiving a plurality of tables corresponding to a plurality of features observed from a dataset for use in machine learning, identifying columns of features within the plurality of tables which are to be combined, concatenating the identified columns of the features based on a union operation which sequentially appends the identified columns to each other, and storing the concatenated columns of the features as a combined dataset.

Abstract: Provided is a system and method for automated feature engineering of multidimensional data. The system and method may generate a flat data set for use in training a machine learning model from a multidimensional data set. In one example, the method may include extracting metadata of multidimensional data, generating declarative statements of features that are to be derived from the multidimensional data based on hierarchical attributes of the multidimensional data which are identified from the extracted metadata, querying, via a query engine, the multidimensional data based on the generated declarative statements to generate the derived features, and storing the derived features in a training data set.

Abstract: Provided is a system and method for identifying and recommending the best hierarchical levels for training a predictive model such as a time-series forecasting model. In one example, the method may include receiving an identification of a measure of multidimensional data, generating a plurality of training data sets that comprise different combinations of hierarchical dimension granularities of aggregation, training a plurality of instances of a machine learning model based on the plurality of training data sets, respectively, and determining and outputting predictive accuracy values of the plurality of instances of the trained machine learning model.

Abstract: Provided is a system and method for combining feature sets of data via single vertical union operation. In one example, the method may include receiving a plurality of tables corresponding to a plurality of features observed from a dataset for use in machine learning, identifying columns of features within the plurality of tables which are to be combined, concatenating the identified columns of the features based on a union operation which sequentially appends the identified columns to each other, and storing the concatenated columns of the features as a combined dataset.

Abstract: According to some embodiments, information may be received about a non-relational database object, such as an InfoSet, InfoSet query, or Advanced Business Application Programming (“ABAP”) function for an enterprise resource planning system. The non-relational database object may then be mapped into a relational database model, such as a model associated with a business intelligence platform. As a result, Structured Query Language (“SQL”) inputs from a business intelligence platform may be used create reports based on information in an enterprise resource planning system.

Abstract: According to some embodiments, information may be received about a non-relational database object, such as an InfoSet, InfoSet query, or Advanced Business Application Programming (“ABAP”) function for an enterprise resource planning system. The non-relational database object may then be mapped into a relational database model, such as a model associated with a business intelligence platform. As a result, Structured Query Language (“SQL”) inputs from a business intelligence platform may be used create reports based on information in an enterprise resource planning system.

Abstract: According to some embodiments, information may be received about a non-relational database object, such as an InfoSet, InfoSet query, or Advanced Business Application Programming (“ABAP”) function for an enterprise resource planning system. The non-relational database object may then be mapped into a relational database model, such as a model associated with a business intelligence platform. As a result, Structured Query Language (“SQL”) inputs from a business intelligence platform may be used create reports based on information in an enterprise resource planning system.

Abstract: According to some embodiments, information may be received about a non-relational database object, such as an InfoSet, InfoSet query, or Advanced Business Application Programming (“ABAP”) function for an enterprise resource planning system. The non-relational database object may then be mapped into a relational database model, such as a model associated with a business intelligence platform. As a result, Structured Query Language (“SQL”) inputs from a business intelligence platform may be used create reports based on information in an enterprise resource planning system.

Abstract: According to some embodiments, information may be received about a non-relational database object, such as an InfoSet, InfoSet query, or Advanced Business Application Programming (“ABAP”) function for an enterprise resource planning system. The non-relational database object may then be mapped into a relational database model, such as a model associated with a business intelligence platform. As a result, Structured Query Language (“SQL”) inputs from a business intelligence platform may be used create reports based on information in an enterprise resource planning system.

Abstract: According to some embodiments, information may be received about a non-relational database object, such as an InfoSet, InfoSet query, or Advanced Business Application Programming (“ABAP”) function for an enterprise resource planning system. The non-relational database object may then be mapped into a relational database model, such as a model associated with a business intelligence platform. As a result, Structured Query Language (“SQL”) inputs from a business intelligence platform may be used create reports based on information in an enterprise resource planning system.