Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data transformation operations by machine learning models, where the predictive data transformation is performed based at least in part on a cross comparison of a pair of columns, accounting for the similarity of both the column names and the column values, inferred by the outputs of a machine learning model. Additionally, certain embodiments of the present invention utilize systems, methods, and computer program products that perform anomaly detection by using machine learning models that operate based at least in part on a comparison of the fixed-size representation of the column values resulting from the machine learning model.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by machine learning models that are trained using one or more filtered training entries that are selected from a plurality of candidate training entries in accordance with one or more optimal imbalance adjustment conditions, where the one or more optimal imbalance adjustment conditions that are selected from a plurality of candidate imbalance adjustment conditions in a manner that is configured to maximize a cumulative target score for the one or more optimal imbalance adjustment conditions while a cumulative non-target score for the one or more optimal imbalance adjustment conditions satisfies an upper cumulative non-target score threshold.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for database integration. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform database integration by utilizing attention-based encoder-decoder machine learning models, such as by performing cross-row linking/similarity determination operations based at least in part on row-wise representations that are generated by combining column-wise representations that are generated by an encoder sub-model of an attention-based encoder-decoder machine learning model, and/or by performing cross-column linking/similarity determination operations based at least in part on column-wise representations that are generated based at least in part on attention scores generated by vertical self-attention sub-models of an attention-based encoder-decoder machine learning model.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to structured data objects. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis with respect to structured data objects by utilizing at least one of cross-table data similarity score generation machine learning models and various machine learning frameworks for cross-database similarity determination.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to structured data objects. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis with respect to structured data objects by utilizing at least one of cross-table data similarity score generation machine learning models and unsupervised anomalous table row detection machine learning models.

Abstract: There is a need for more effective and efficient anomalous text detection.