Abstract: Embodiments of the present disclosure provide systems and methods for matching data entities across disparate datasets. One method may include generating a reference numeric vector for a reference data object and generating a plurality of match candidate numeric vectors for a plurality of match candidate data objects. The method may also include identifying a subset of match candidate data objects from the plurality of match candidate data objects and generating a plurality of data features based on a comparison between one or more reference object attributes corresponding to the reference data object and one or more matching object attributes corresponding to the subset of match candidate data objects. The method may also include identifying a match between the reference data object and a match candidate data object of the subset of match candidate data objects based on the plurality of data features.

Abstract: Various embodiments of the present disclosure provide data storage, processing, and prediction techniques for providing predictive insights within large data prediction domains. The techniques may include generating, using a plurality of source tables for a prediction domain, a global graph for the prediction domain. The techniques may include generating, using a graph-based machine learning model, a plurality of node-level weights for the plurality of graph nodes based on a plurality of node attributes corresponding to the plurality of graph nodes. The techniques may include generating, using the graph-based machine learning model, a plurality of semantic-level weights for the plurality of weighted edges based on a designated predictive task for the global graph. The techniques may include generating plurality of graph node embeddings and initiating the performance of the designated predictive task based on the plurality of graph node embeddings.

Abstract: Embodiments of the present disclosure provide systems and methods for matching data entities across disparate datasets. One method may include generating a reference numeric vector for a reference data object and generating a plurality of match candidate numeric vectors for a plurality of match candidate data objects. The method may also include identifying a subset of match candidate data objects from the plurality of match candidate data objects and generating a plurality of data features based on a comparison between one or more reference object attributes corresponding to the reference data object and one or more matching object attributes corresponding to the subset of match candidate data objects. The method may also include identifying a match between the reference data object and a match candidate data object of the subset of match candidate data objects based on the plurality of data features.

Abstract: Various embodiments of the present disclosure provide data storage, processing, and prediction techniques for providing predictive insights within large data prediction domains. The techniques may include generating, using a plurality of source tables for a prediction domain, a global graph for the prediction domain. The techniques may include generating, using a graph-based machine learning model, a plurality of node-level weights for the plurality of graph nodes based on a plurality of node attributes corresponding to the plurality of graph nodes. The techniques may include generating, using the graph-based machine learning model, a plurality of semantic-level weights for the plurality of weighted edges based on a designated predictive task for the global graph. The techniques may include generating plurality of graph node embeddings and initiating the performance of the designated predictive task based on the plurality of graph node embeddings.

Abstract: Various embodiments of the present disclosure provide data storage, processing, and prediction techniques for providing predictive insights within large data prediction domains. The techniques may include generating, using a plurality of source tables for a prediction domain, a plurality of subdomain-specific graphs for the prediction domain. The techniques may include generating a plurality of subdomain-specific embeddings for the plurality of subdomain-specific graphs and a composite graph embedding based on the plurality of graph embeddings and a designated predictive task. The techniques may include initiating the performance of the designated predictive task based on the composite graph embedding.

Abstract: Embodiments of the present disclosure provide systems and methods for matching data entities across disparate datasets. One method may include generating a reference numeric vector for a reference data object and generating a plurality of match candidate numeric vectors for a plurality of match candidate data objects. The method may also include identifying a subset of match candidate data objects from the plurality of match candidate data objects and generating a plurality of data features based on a comparison between one or more reference object attributes corresponding to the reference data object and one or more matching object attributes corresponding to the subset of match candidate data objects. The method may also include identifying a match between the reference data object and a match candidate data object of the subset of match candidate data objects based on the plurality of data features.

Abstract: Various embodiments of the present disclosure provide data storage, processing, and prediction techniques for providing predictive insights within large data prediction domains. The techniques may include generating, using a plurality of source tables for a prediction domain, a global graph for the prediction domain. The techniques may include generating, using a graph-based machine learning model, a plurality of node-level weights for the plurality of graph nodes based on a plurality of node attributes corresponding to the plurality of graph nodes. The techniques may include generating, using the graph-based machine learning model, a plurality of semantic-level weights for the plurality of weighted edges based on a designated predictive task for the global graph. The techniques may include generating plurality of graph node embeddings and initiating the performance of the designated predictive task based on the plurality of graph node embeddings.