Abstract: A feature engineering tool automatically generates a group of features for training a machine learning model. The feature engineering tool selects primitives for a dataset and synthesizes a plurality of features based on the selective primitives and the dataset. The feature engineering tool iteratively applies the plurality of features to a different portion of the dataset to evaluate usefulness of the plurality of features. Based on the evaluation, it removes some of the plurality of features to obtain the group of features. The feature engineering tool also determines the importance factor for each feature in the group. The machine learning model is generated based on the features and their importance factors and can be used to make a prediction based on new data.

Abstract: A machine learning application is selected from a plurality of machine learning applications. Each machine learning application corresponds to a different industry problem and includes standard features and machine learning pipelines specific to the corresponding industrial problem. The machine learning application receives a dataset for generating a model for making a prediction for the industrial problem corresponding to the selected machine learning application. The standard features are provided for display for the user to map variables in the dataset to the standard features. Mapping by the user is received through the user interface. The machine learning pipelines are applied to the dataset to train a plurality of models based at least on the mapping. The trained models are ranked and one of the trained models is selected based on the ranking. The selected trained model is to be used for making the prediction based on new data.

Abstract: A feature engineering application receives a plurality of data sets from different data sources for training a model for making a prediction based on new data. The feature engineering application generates primitives based on the data sets. A primitive is to be applied to a variable in the data sets to synthesize a feature. The feature engineering application also receives a temporal parameter that specifies a temporal value for generating time-based features. After the primitives are generated and the temporal parameter is received, the feature engineering application aggregates the plurality of data entities based on primary variables in the plurality of data entities and generate an entity set based on the aggregation. The feature engineering application then synthesize features, including the time-based features, based on the entity set, at least some of the primitives, and the temporal parameter.

Abstract: A feature engineering tool automatically generates a group of features for training a machine learning model. The feature engineering tool selects primitives for a dataset and synthesizes a plurality of features based on the selective primitives and the dataset. The feature engineering tool iteratively applies the plurality of features to a different portion of the dataset to evaluate usefulness of the plurality of features. Based on the evaluation, it removes some of the plurality of features to obtain the group of features. The feature engineering tool also determines the importance factor for each feature in the group. The machine learning model is generated based on the features and their importance factors and can be used to make a prediction based on new data.

Abstract: A relational database is transformed so as to obfuscate secure and/or private aspects of data contained in the database, while preserving salient elements of the data to facilitate data analysis. A restructured database is generatively modeled, and the model is sampled to create synthetic data that maintains sufficiently similar (or the same) mathematical properties and relations as the original data stored in the database. In one example, various statistics at the intersection of related database tables are determined by modeling data using an iterative multivariate approach. Synthetic data may be sampled from any part of the modeled database, wherein the synthesized data is “realistic” in that it statistically mimics the original data in the database. The generation of such synthetic data allows publication of bulk data freely and on-demand (e.g., for data analysis purposes), without the risk of security/privacy breaches.

Abstract: A relational database is transformed so as to obfuscate secure and/or private aspects of data contained in the database, while preserving salient elements of the data to facilitate data analysis. A restructured database is generatively modeled, and the model is sampled to create synthetic data that maintains sufficiently similar (or the same) mathematical properties and relations as the original data stored in the database. In one example, various statistics at the intersection of related database tables are determined by modeling data using an iterative multivariate approach. Synthetic data may be sampled from any part of the modeled database, wherein the synthesized data is “realistic” in that it statistically mimics the original data in the database. The generation of such synthetic data allows publication of bulk data freely and on-demand (e.g., for data analysis purposes), without the risk of security/privacy breaches.