Abstract: An example operation includes one or more of determining by a primary computing node that an en route vehicle will be delayed for a time-sensitive event, coordinating by the primary computing node with a secondary computing node at an event location and the en route vehicle to provide a real-time event experience to at least one device in the en route vehicle, managing by the primary computing node the real-time event experience between the secondary computing node and the at least one device in the en route vehicle, transferring by the primary computing node the real-time event experience to the secondary computing node when a device of the at least one device is in proximity to the secondary computing node, and terminating by the secondary computing node the real-time event experience when the at least one device reaches a specific location within the event location.

Abstract: An example operation includes one or more of determining data to be provided to a vehicle, determining data to be retrieved from the vehicle, determining a route for the vehicle based on an amount of time the data is provided to the vehicle and the data is retrieved from the vehicle, a speed of the vehicle, and a location of at least one computing node that is configured to provide and retrieve the data.

Abstract: An example operation includes one or more of determining, by a first node, that an event has occurred in a geographic area, predicting, by the first node, a severity of the event, a duration of the event, and at least one vehicle associated with the event, sending, by the first node, the prediction to a second node and a time the at least one vehicle will be proximate the second node, and sending by the second node, notifications to other vehicles proximate the second node to maneuver based on the prediction, prior to the time.

Abstract: An example operation includes one or more of determining, by a vehicle, that an occupant assist application is operating in the vehicle to assist a vehicle occupant during vehicle operation, determining, by the vehicle, that an unsafe driving condition is likely to occur via a monitoring application, prior to a time that the unsafe driving condition is expected to occur, ceasing, by the vehicle, the occupant assist application, and executing, by the vehicle, a driving assist application to assist with the vehicle operation during the unsafe driving condition.

Abstract: An example operation includes one or more of monitoring, via a vehicle, a behavior of an occupant of the vehicle, responsive to the behavior being above a threshold, generating via an application of the vehicle, a first suggested action intended for the occupant based on the behavior and an amount the behavior is above the threshold, monitoring, by the vehicle, whether the first suggested action is being performed by the occupant, determining an alternate action is being performed by the occupant, generating a second suggested action based on the alternate action and the first suggested action, and providing a value to an occupant device associated with the occupant responsive to the second action being detected by the transport.

Abstract: An example operation includes one or more of receiving a first response, from an application associated with a vehicle, including one or more factual statements determined by the application based on a set of data, retrieving vehicle data from the vehicle related to the factual statements, parsing the first response into one or more portions of the one or more factual statements, comparing the one or more portions to other portions of other factual statements included in a first knowledgebase containing other factual statements, wherein the other factual statements are based on the vehicle data, and receiving a second response, from the application, including one or more amended factual statements based on the comparing.

Abstract: An example operation includes one or more of identifying, via a vehicle application of a vehicle, one or more topics of interest associated with a vehicle occupant based one or more vehicle occupant behaviors detected by the vehicle, applying, via the vehicle application, one or more enhanced services to a device associated with vehicle occupant based on the one or more topics of interest and an occupant profile, and modifying, via the vehicle application, the one or more enhanced services based on the device location and changes to the one or more vehicle occupant behaviors.

Abstract: An example operation includes one or more of receiving information at a vehicle in proximity to a first node from the first node, sending information from the vehicle to the first node, analyzing the sent information by the first node, determining by the first node that the vehicle will be in proximity to an adjacent node to the first node, after the vehicle is no longer in proximity to the first node, receiving the analyzed information from the first node at the adjacent node, constructing information related to the received analyzed information at the adjacent node, and sending the constructed information to the vehicle from the adjacent node.

Abstract: An example operation includes one or more of accessing, by an application providing assistance to a vehicle, sensor data associated with an environment inside and outside of a vehicle and profile data associated with a vehicle occupant, determining, by the application, an initial condition of the vehicle occupant based on the sensor data and the profile data, responsive to the initial condition being above a health condition threshold, accessing, by the application, health data associated with the vehicle occupant from a mobile device, determining, by the application, an updated condition of the vehicle occupant based on the health data, creating, by the application, an alert to notify the occupant based on the updated condition and one or more current driving conditions of the vehicle identified by the sensor data, and performing, by the vehicle, one or more vehicle actions based on the alert and the one or more current driving conditions.

Abstract: An example operation includes one or more of establishing a communication between a first vehicle and a computing node, determining data to be transmitted from the computing node to the first vehicle responsive to a portion of the data being stored on a second vehicle, determining the second vehicle will be in proximity to the first vehicle, and transmitting the portion of the data from the second vehicle to the first vehicle when the vehicles are in proximity and a remaining portion of the data from the computing node to the first vehicle.

Abstract: A building device of a building includes or is in communication with a processing circuit configured to store one or more files, each of the one or more files comprising instructions and a ledger, the ledger comprising information describing at least one of the one or more files. The processing circuit is configured to verify the one or more files by retrieving a root ledger from storage of the processing circuit, wherein the root ledger comprises information based on one or more characteristics of a blockchain, reassembling the blockchain based on the ledger of each of the one or more files, verifying the blockchain with the root ledger, and verifying the one or more files with the verified blockchain. The processing circuit is configured to execute the instructions of the one or more files in response to a determination that the one or more files are verified.

Abstract: A building device of a building includes or is in communication with a processing circuit configured to store one or more files, each of the one or more files comprising instructions and a ledger, the ledger comprising information describing at least one of the one or more files. The processing circuit is configured to verify the one or more files by retrieving a root ledger from storage of the processing circuit, wherein the root ledger comprises information based on one or more characteristics of a blockchain, reassembling the blockchain based on the ledger of each of the one or more files, verifying the blockchain with the root ledger, and verifying the one or more files with the verified blockchain. The processing circuit is configured to execute the instructions of the one or more files in response to a determination that the one or more files are verified.

Abstract: A stochastic computational model parameter synthesis system comprising at least one processor capable of executing processor executable code, and a non-transitory computer memory operably coupled with the at least one processor and storing processor executable code, which when executed by processor, causes processor to synthesize at least one parameter of a stochastic computational model to satisfy one or more behavioral specifications of properties observed in a modeled system. The processor generates and searches randomized projections of a first parameter space having n dimensions into one or more second abstract parameter space having d dimensions, where d is less than n, and outputs a signal indicative of a synthesized parameter value to the user.

Abstract: An object relational map verification system is described. In some embodiments, the object relational map verification system can verify object relational maps and identify counterexamples when an object relational map cannot be verified. The object relational map verification system can verify an object relational map by (1) receiving objects, database schemas, query views, and update views; (2) generating first order logic formulae corresponding to the received objects, database schemas, query views, and update views; and (3) proving theorems indicated by the generated first order logic formulae. When the theorems are proved, the object relational map is verified. In some embodiments, the object relational map verification system can also generate models illustrating counterexamples when the theorem cannot be proved. The counterexamples provide data that the object relational map does not consistently store and then retrieve.

Abstract: An object relational map verification system is described. In some embodiments, the object relational map verification system can verify object relational maps and identify counterexamples when an object relational map cannot be verified. The object relational map verification system can verify an object relational map by (1) receiving objects, database schemas, query views, and update views; (2) generating first order logic formulae corresponding to the received objects, database schemas, query views, and update views; and (3) proving theorems indicated by the generated first order logic formulae. When the theorems are proved, the object relational map is verified. In some embodiments, the object relational map verification system can also generate models illustrating counterexamples when the theorem cannot be proved. The counterexamples provide data that the object relational map does not consistently store and then retrieve.