Abstract: A system and method of autonomous data hub processing that uses semantic metadata, machine learning models, and a permissioned blockchain to autonomously standardize, identify and correct errors in supply chain data is disclosed. Embodiments input supply chain data stored in a supply chain database, train with the machine learning model trainer, one or more machine learning models to identify one or more data errors in the supply chain data, clean the one or more identified data errors from the supply chain data, and store cleaned supply chain data. Embodiments also update one or more machine learning models to identify one or more data errors in cleaned supply chain data, and join and aggregate one or more sets of cleaned supply chain data.

Abstract: A system and method are disclosed for providing supply chain intelligence based on a semantic supply chain model. The method includes building a semantic model of a supply chain, building goals and measures to construct measure graphs to represent supply chain scenarios, storing access and computation information for the measures, relating the measures to the supply chain goals, monitoring the measures associated with the supply chain goals; tuning the measures using machine learning models by tracking outcomes and user actions associated with the measures and goals to update the machine learning models based on the tracked outcomes and user actions, monitoring for abnormal patterns of the measures, triggering, based on a detection of an abnormal pattern, an alert and a resolution, and rendering an alert or a resolution in machine form to supply chain execution systems.

Abstract: A system and method of autonomous data hub processing that uses semantic metadata, machine learning models, and a permissioned blockchain to autonomously standardize, identify and correct errors in supply chain data is disclosed. Embodiments input supply chain data stored in a supply chain database, train with the machine learning model trainer, one or more machine learning models to identify one or more data errors in the supply chain data, clean the one or more identified data errors from the supply chain data, and store cleaned supply chain data. Embodiments also update one or more machine learning models to identify one or more data errors in cleaned supply chain data, and join and aggregate one or more sets of cleaned supply chain data.

Abstract: A system and method are disclosed for providing supply chain intelligence based on a semantic supply chain model. The method includes building a semantic model of a supply chain, building goals and measures to construct measure graphs to represent supply chain scenarios, storing access and computation information for the measures, relating the measures to the supply chain goals, monitoring the measures associated with the supply chain goals; tuning the measures using machine learning models by tracking outcomes and user actions associated with the measures and goals to update the machine learning models based on the tracked outcomes and user actions, monitoring for abnormal patterns of the measures, triggering, based on a detection of an abnormal pattern, an alert and a resolution, and rendering an alert or a resolution in machine form to supply chain execution systems.

Abstract: A system and method is disclosed for providing data staging and loading through a master data management (MDM) system coupled with one or more business entities. The system is operable to invoke batch data loading of data associated with one or more source systems associated with the one or more business entities, into an input staging area. The system is further operable to load the data from the input staging area into a master repository and subsequently load the data from the master repository into an output staging area.

Abstract: A system and method of autonomous data hub processing that uses semantic metadata, machine learning models, and a permissioned blockchain to autonomously standardize, identify and correct errors in supply chain data is disclosed. Embodiments input supply chain data stored in a supply chain database, train with the machine learning model trainer, one or more machine learning models to identify one or more data errors in the supply chain data, clean the one or more identified data errors from the supply chain data, and store cleaned supply chain data. Embodiments also update one or more machine learning models to identify one or more data errors in cleaned supply chain data, and join and aggregate one or more sets of cleaned supply chain data.

Abstract: A system and method is disclosed for providing data staging and loading through a master data management (MDM) system coupled with one or more business entities. The system is operable to invoke batch data loading of data associated with one or more source systems associated with the one or more business entities, into an input staging area. The system is further operable to load the data from the input staging area into a master repository and subsequently load the data from the master repository into an output staging area.

Abstract: A system and method of autonomous data hub processing that uses semantic metadata, machine learning models, and a permissioned blockchain to autonomously standardize, identify and correct errors in supply chain data is disclosed. Embodiments input supply chain data stored in a supply chain database, train with the machine learning model trainer, one or more machine learning models to identify one or more data errors in the supply chain data, clean the one or more identified data errors from the supply chain data, and store cleaned supply chain data. Embodiments also update one or more machine learning models to identify one or more data errors in cleaned supply chain data, and join and aggregate one or more sets of cleaned supply chain data.

Abstract: A system and method is disclosed for providing data staging and loading through a master data management (MDM) system coupled with one or more business entities. The system is operable to invoke batch data loading of data associated with one or more source systems associated with the one or more business entities, into an input staging area. The system is further operable to load the data from the input staging area into a master repository and subsequently load the data from the master repository into an output staging area.

Abstract: A system and method are disclosed for real-time multi-facet interactive planning. The system includes a database that stores data associated with one or more entities. The system further includes a computer having a memory and a processor coupled with the database and configured to receive a facet value modification and modify the facet value based on a new facet value. The computer is further configured to propagate the new facet value to modify another facet value.

Abstract: A system and method is disclosed for providing data staging and loading through a master data management (MDM) system coupled with one or more business entities. The system is operable to invoke batch data loading of data associated with one or more source systems associated with the one or more business entities, into an input staging area. The system is further operable to load the data from the input staging area into a master repository and subsequently load the data from the master repository into an output staging area.

Abstract: A system and method are disclosed for real-time multi-facet interactive planning. The system includes a database that stores data associated with one or more entities. The system further includes a computer having a memory and a processor coupled with the database and configured to receive a facet value modification and modify the facet value based on a new facet value. The computer is further configured to propagate the new facet value to modify another facet value.

Abstract: A system and method is disclosed for providing data staging and loading through a master data management (MDM) system coupled with one or more business entities. The system is operable to invoke batch data loading of data associated with one or more source systems associated with the one or more business entities, into an input staging area. The system is further operable to load the data from the input staging area into a master repository and subsequently load the data from the master repository into an output staging area.

Abstract: A system and method are disclosed for real-time multi-facet interactive planning. The system includes a database that stores data associated with one or more entities. The system further includes a computer having a memory and a processor coupled with the database and configured to receive a facet value modification and modify the facet value based on a new facet value. The computer is further configured to propagate the new facet value to modify another facet value.

Abstract: A system and method is disclosed for providing data staging and loading through a master data management (MDM) system coupled with one or more business entities. The system is operable to invoke batch data loading of data associated with one or more source systems associated with the one or more business entities, into an input staging area. The system is further operable to load the data from the input staging area into a master repository and subsequently load the data from the master repository into an output staging area.

Abstract: A system (10) for enabling remote monitoring and management of one or more applications (42) within a domain (30) includes one or more computers (40) that execute one or more applications (42) being monitored and managed. The system (10) also includes a firewall (50) that limits access to the applications (42). The system (10) further includes an application management layer (70) within the domain (30) that includes one or more agents (72) that each monitor applications (42) and that generate notifications in response to the occurrence of events associated with the applications (42). The system (10) also includes a communication layer (60) within the domain (30) that retrieves and communicates the notifications to a monitoring and management portal (20) using a communication protocol providing access through the firewall (50). The communication layer (60) may also receive a command for a particular application (42) and communicate the command to an agent (72) associated with the application (42).

Abstract: A system (10) for enabling remote monitoring and management of one or more applications (42) within a domain (30) includes one or more computers (40) that execute one or more applications (42) being monitored and managed. The system (10) also includes a firewall (50) that limits access to the applications (42). The system (10) further includes an application management layer (70) within the domain (30) that includes one or more agents (72) that each monitor applications (42) and that generate notifications in response to the occurrence of events associated with the applications (42). The system (10) also includes a communication layer (60) within the domain (30) that retrieves and communicates the notifications to a monitoring and management portal (20) using a communication protocol providing access through the firewall (50). The communication layer (60) may also receive a command for a particular application (42) and communicate the command to an agent (72) associated with the application (42).