Abstract: A new declarative approach to business intelligence (BI) and reporting focuses on streamlining the embeddability of BI and reporting (BIR) into an application (e.g., web, mobile, etc.) and allows a developer to use a REST API and declare, via a YAML file, the type of BIR dashboard, report, metric or set of metrics desired. A YAML descriptor interpreter interprets the YAML file, collaborates with a BIR server at the backend to dynamically construct the requested content, and repackages and returns the requested content in a REST API response. This new method allows application developers to avoid any need of pre-designing reports, dashboards, etc., and eliminates the need of application developers to embed client-side scripting libraries, significantly reduces the amount of effort, time, and complexity in using a BI and reporting service. Application developers simply declare what they need, and the service returns the content exactly as described in the request.

Abstract: A system for electronic data interchange (EDI) management includes a memory for storing the EDI document data and a machine learning model representing a set of features of EDI documents and a corresponding status. The system further includes a processor and a non-transitory computer readable medium storing instructions for: accessing an EDI file, the EDI file comprising envelope metadata for an envelope and a first EDI document: and translating the EDI file into a first translated EDI document containing the envelope metadata and a set of EDI document data extracted from the first EDI document, the first translated EDI document formatted according to a hierarchical structure comprising attributes translatable into features processable by the machine learning model to determine a status of the first EDI document.

Abstract: A system for electronic data interchange (EDI) management includes a memory for storing the EDI document data and a machine learning model representing a set of features of EDI documents and a corresponding status. The system further includes a processor and a non-transitory computer readable medium storing instructions for: accessing an EDI file, the EDI file comprising envelope metadata for an envelope and a first EDI document; and translating the EDI file into a first translated EDI document containing the envelope metadata and a set of EDI document data extracted from the first EDI document, the first translated EDI document formatted according to a hierarchical structure comprising attributes translatable into features processable by the machine learning model to determine a status of the first EDI document.

Abstract: A system for electronic data interchange (EDI) management includes a memory for storing the EDI document data and a machine learning model representing a set of features of EDI documents and a corresponding status. The system further includes a processor and a non-transitory computer readable medium storing instructions for: accessing an EDI file, the EDI file comprising envelope metadata for an envelope and a first EDI document; and translating the EDI file into a first translated EDI document containing the envelope metadata and a set of EDI document data extracted from the first EDI document, the first translated EDI document formatted according to a hierarchical structure comprising attributes translatable into features processable by the machine learning model to determine a status of the first EDI document.

Abstract: An electronic data interchange (EDI) management system may comprise a memory for storing EDI document data and a machine learning prediction model representing element information of EDI documents of a first type and a corresponding status. A processor can be configured to extract elements from an EDI document, create a document record for the EDI document, the document record comprising elements extracted from the EDI document; determine a first status for the EDI document of the first type by processing the extracted elements using a machine learning model, the machine learning model trained on a training set of elements to classify documents according to a plurality of statuses, and add the first status to the document record for the EDI document of the first type, the first status accessible to a client computer via a presentation layer.

Abstract: An electronic data interchange (EDI) management system may comprise a memory for storing EDI document data and a machine learning prediction model representing element information of EDI documents of a first type and a corresponding status. A processor can be configured to extract elements from an EDI document, create a document record for the EDI document, the document record comprising elements extracted from the EDI document; determine a first status for the EDI document of the first type by processing the extracted elements using a machine learning model, the machine learning model trained on a training set of elements to classify documents according to a plurality of statuses, and add the first status to the document record for the EDI document of the first type, the first status accessible to a client computer via a presentation layer.

Abstract: An electronic data interchange (EDI) management system may comprise a memory for storing EDI document data and a machine learning prediction model representing element information of EDI documents of a first type and a corresponding status. A processor can be configured to extract elements from an EDI document, create a document record for the EDI document, the document record comprising elements extracted from the EDI document; determine a first delivery status for the EDI document of the first type by processing the extracted elements using a machine learning delivery model, the machine learning delivery model trained on a training set of elements to classify documents according to a plurality of delivery statuses, and add the first status to the document record for the EDI document of the first type, the first status accessible to a client computer via a presentation layer.

Abstract: An electronic data interchange (EDI) management system may comprise a memory for storing EDI document data and a machine learning prediction model representing element information of EDI documents of a first type and a corresponding status. A processor can be configured to extract elements from an EDI document, create a document record for the EDI document, the document record comprising elements extracted from the EDI document; determine a first delivery status for the EDI document of the first type by processing the extracted elements using a machine learning delivery model, the machine learning delivery model trained on a training set of elements to classify documents according to a plurality of delivery statuses, and add the first status to the document record for the EDI document of the first type, the first status accessible to a client computer via a presentation layer.

Abstract: An electronic data interchange (EDI) management system may comprise a memory for storing EDI document data and a machine learning prediction model representing element information of EDI documents of a first type and a corresponding status. A processor can be configured to extract segments and elements from an EDI document, create a document record for the EDI document, the document record comprising a hierarchy of segments and elements extracted from the EDI document; create a feature vector for the EDI document from the elements extracted from the EDI document, determine a first status for the EDI document by processing the feature vector for the EDI document using the prediction model and add the first status to the document record for the EDI document of the first type, the first status accessible to a client computer via a presentation layer.

Abstract: An electronic data interchange (EDI) management system may comprise a memory for storing EDI document data and a machine learning prediction model representing element information of EDI documents of a first type and a corresponding status. A processor can be configured to extract segments and elements from an EDI document, create a document record for the EDI document, the document record comprising a hierarchy of segments and elements extracted from the EDI document; create a feature vector for the EDI document from the elements extracted from the EDI document, determine a first status for the EDI document by processing the feature vector for the EDI document using the prediction model and add the first status to the document record for the EDI document of the first type, the first status accessible to a client computer via a presentation layer.