Abstract: A method, apparatus, system, and computer program code for forecasting a data anomaly to a supply chain. A plurality of data records is identified for a plurality of entities. The data records include import records and export records. The data fields in the data records are categorized into generic field types. The generic field types include numeric fields, categorical fields, and date fields. For each of the plurality of entities, an entity-specific model is constructed for forecasting imports and exports based on the generic field types. The entity-specific model for each of the plurality of entities is combined into a global supply chain model. Based on the global supply chain model, a data anomaly is forecast to a supply chain that is associated with a particular entity.

Abstract: A method, apparatus, system, and computer program code for retrieving data records. A set of static configuration objects is provided, including: a set of resources that describe available data items, and a set of views that express a serialized transformation of resources objects into a response. In response to receiving a data request, a computer system generates a data fetch execution plan from the set of resources and the set of views. The data fetch execution plan is generated using an executor adapted to a particular data store and set of performance requirements. The computer system retrieves the data records according to the data fetch execution plan.

Abstract: A method, apparatus, system, and computer program code for retrieving data records. A set of static configuration objects is provided, including: a set of resources that describe available data items, and a set of views that express a serialized transformation of resources objects into a response. In response to receiving a data request, a computer system generates a data fetch execution plan from the set of resources and the set of views. The data fetch execution plan is generated using an executor adapted to a particular data store and set of performance requirements. The computer system retrieves the data records according to the data fetch execution plan.

Abstract: A method for identifying a parent company in a corporate hierarchy is provided. A neural network embeds a number of company names into respective numeric vectors and then indexes the vectors into an approximate nearest neighbors (ANN) index according to euclidean distances between them. In response to a query regarding a parent company of a specified query company, a top N number of nearest neighbor companies to the query company is extracted from the ANN index. A machine learning voting model determines which, if any, of the extracted nearest neighbor companies has a parent company that best corresponds to the query company. If an extracted nearest neighbor company has a parent company that best corresponds to the query company, the parent company is displayed to a user through a user interface.

Abstract: A method, apparatus, system, and computer program code for forecasting a data anomaly to a supply chain. A plurality of data records is identified for a plurality of entities. The data records include import records and export records. The data fields in the data records are categorized into generic field types. The generic field types include numeric fields, categorical fields, and date fields. For each of the plurality of entities, an entity-specific model is constructed for forecasting imports and exports based on the generic field types. The entity-specific model for each of the plurality of entities is combined into a global supply chain model. Based on the global supply chain model, a data anomaly is forecast to a supply chain that is associated with a particular entity.

Abstract: An apparatus includes a data access circuit that interprets data records, each having a number of data fields, a record parsing circuit that determines a number of n-grams from terms of each of the data records and maps the number of n-grams to a corresponding number of mathematical vectors, and a record association circuit that determines whether a similarity value between a first mathematical vector for the first data record and a second mathematical vector for the second data record is greater than a threshold similarity value, and associates the first and second data records in response to the similarity value exceeding the threshold similarity value. An example apparatus includes a reporting circuit that provides a catalog entity identifier, associates each of the first term and the second term to the catalog entity identifier, and provides a summary of activity for an entity.

Abstract: An apparatus includes a data access circuit that interprets data records, each having a number of data fields, a record parsing circuit that determines a number of n-grams from terms of each of the data records and maps the number of n-grams to a corresponding number of mathematical vectors, and a record association circuit that determines whether a similarity value between a first mathematical vector for the first data record and a second mathematical vector for the second data record is greater than a threshold similarity value, and associates the first and second data records in response to the similarity value exceeding the threshold similarity value. An example apparatus includes a reporting circuit that provides a catalog entity identifier, associates each of the first term and the second term to the catalog entity identifier, and provides a summary of activity for an entity.