Abstract: Systems, methods, and processing nodes predicting and perform preventive maintenance in a transportation system. Predicting and performing preventive maintenance in a transportation system includes determining historical data for electronic devices in the transportation system. Predicting and performing preventive maintenance also includes determining dependencies of the electronic devices based on the historical data. Predicting and performing preventive maintenance includes determining a likelihood of a fault in the target electronic device during a time period based on the dependencies of the electronic devices and a mutual probability of failure of the target electronic device and parent electronic devices associated with the target electronic device. Predicting and performing preventive maintenance also includes initiating preemptive maintenance on the target electronic device based on the likelihood of the fault.

Abstract: A historical task database relating vehicle rollout decisions, vehicle maintenance states and subsequent deteriorations is created. A pattern analyzer may use an item-set mining algorithm on the task database to recommend whether a vehicle with its current maintenance state should be deployed. A supervisor uses this recommendation to make a rollout decision. These decisions are added to the database. Heuristic rules are defined to determine if the rollout decision was correct. The system to learns when a supervisor continues to make costly rollout errors. The system also discovers combinations of defects that lead to a rapid deterioration and makes recommendations that the vehicle be sent for maintenance rather than being rolled out.

Abstract: Systems, methods, and processing nodes determine and perform preventive maintenance on a transport vehicle in a transportation system. The method includes extracting features for previous incidents that have occurred to a plurality of transport vehicles in the transportation system. The method also includes determining a criticality of types of incidents based on the features extracted. The method includes predicting, based on the criticality of types of incidents and the features extracted, details of at least one future incident for a first transport vehicle from the plurality of transport vehicles. The details include a predicted type of the at least one future incident, a predicted time of the at least one future incident, and a predicted criticality of the at least one future incident. Additionally, the method includes performing a prescriptive action for the first transport vehicle to mitigate the at least one future incident in the first transport vehicle.

Abstract: System and methods for personalizing an application include an application server for hosting an application that is executed on a wireless device, the server being configured to monitor usage of a user interface including an interaction between a user of the application and one or more user interface elements and present an updated user interface comprising one or more additional user-interface elements based on the usage.

Abstract: A historical task database relating vehicle rollout decisions, vehicle maintenance states and subsequent deteriorations is created. A pattern analyzer may use an item-set mining algorithm on the task database to recommend whether a vehicle with its current maintenance state should be deployed. A supervisor uses this recommendation to make a rollout decision. These decisions are added to the database. Heuristic rules are defined to determine if the rollout decision was correct. The system to learns when a supervisor continues to make costly rollout errors. The system also discovers combinations of defects that lead to a rapid deterioration and makes recommendations that the vehicle be sent for maintenance rather than being rolled out.

Abstract: Systems, methods, and processing nodes determine and perform preventive maintenance on a transport vehicle in a transportation system. The method includes extracting features for previous incidents that have occurred to a plurality of transport vehicles in the transportation system. The method also includes determining a criticality of types of incidents based on the features extracted. The method includes predicting, based on the criticality of types of incidents and the features extracted, details of at least one future incident for a first transport vehicle from the plurality of transport vehicles. The details include a predicted type of the at least one future incident, a predicted time of the at least one future incident, and a predicted criticality of the at least one future incident. Additionally, the method includes performing a prescriptive action for the first transport vehicle to mitigate the at least one future incident in the first transport vehicle.

Abstract: Systems, methods, and processing nodes predicting and perform preventive maintenance in a transportation system. Predicting and performing preventive maintenance in a transportation system includes determining historical data for electronic devices in the transportation system. Predicting and performing preventive maintenance also includes determining dependencies of the electronic devices based on the historical data. Predicting and performing preventive maintenance includes determining a likelihood of a fault in the target electronic device during a time period based on the dependencies of the electronic devices and a mutual probability of failure of the target electronic device and parent electronic devices associated with the target electronic device. Predicting and performing preventive maintenance also includes initiating preemptive maintenance on the target electronic device based on the likelihood of the fault.

Abstract: System and methods for personalizing an application include an application server for hosting an application that is executed on a wireless device, the server being configured to monitor usage of a user interface including an interaction between a user of the application and one or more user interface elements and present an updated user interface comprising one or more additional user-interface elements based on the usage.