Abstract: The present disclosure provides systems and methods that impute planned transshipment locations for an itinerary associated with an item of cargo. In particular, according to one aspect of the present disclosure, a supply chain management computing system can obtain itinerary data that describes a planned shipment of an item of cargo from an origin location to a destination location. For example, the itinerary data can identify at least a shipping vehicle planned to transport the item of cargo. The supply chain management computing system can access vehicle location data associated with at least the shipping vehicle and can predict, based at least in part on the itinerary data and the vehicle location data, a transshipment location at which the item of cargo is transferred from the shipping vehicle to a different shipping vehicle.

Abstract: The present disclosure provides systems and methods that impute planned transshipment locations for an itinerary associated with an item of cargo. In particular, according to one aspect of the present disclosure, a supply chain management computing system can obtain itinerary data that describes a planned shipment of an item of cargo from an origin location to a destination location. For example, the itinerary data can identify at least a shipping vehicle planned to transport the item of cargo. The supply chain management computing system can access vehicle location data associated with at least the shipping vehicle and can predict, based at least in part on the itinerary data and the vehicle location data, a transshipment location at which the item of cargo is transferred from the shipping vehicle to a different shipping vehicle.

Abstract: The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world.

Abstract: The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world.

Abstract: The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world.

Abstract: The present disclosure provides systems and methods that impute planned transshipment locations for an itinerary associated with an item of cargo. In particular, according to one aspect of the present disclosure, a supply chain management computing system can obtain itinerary data that describes a planned shipment of an item of cargo from an origin location to a destination location. For example, the itinerary data can identify at least a shipping vehicle planned to transport the item of cargo. The supply chain management computing system can access vehicle location data associated with at least the shipping vehicle and can predict, based at least in part on the itinerary data and the vehicle location data, a transshipment location at which the item of cargo is transferred from the shipping vehicle to a different shipping vehicle.

Abstract: A method, system, apparatus, article of manufacture, and computer program product provide the ability to ingest a media content file. The media content file to be uploaded and managed in an enterprise media framework (EMF) is selected. Media content file(s) to be tagged are also selected. A mask matcher identifies a mask (having multiple parts) that identifies a file structure of information associated with the media content file. For each of the multiple parts and based on the information associated with the media content file, metadata is calculated and applied to the media content file.

Abstract: A method, system, apparatus, article of manufacture, and computer program product provide the ability to ingest a media content file. The media content file to be uploaded and managed in an enterprise media framework (EMF) is selected. Media content file(s) to be tagged are also selected. A mask matcher identifies a mask (having multiple parts) that identifies a file structure of information associated with the media content file. For each of the multiple parts and based on the information associated with the media content file, metadata is calculated and applied to the media content file.