Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may identify, by an event agent (EA), an event occurring in a geographic space in which a plurality of mobile objects move. The embodiment may determine the event is an expected event based on predicting time-series changes of the event handled by the EA. The embodiment may manage, by a predictive environment agent (PEA), the expected event.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for detecting anomalous vehicle behavior. It is determined whether a plurality of received car probes include one or more indicators of unusual behavior. An object agent corresponding to a vehicle having a car probe that includes an indicator of unusual behavior is selected. A search is performed to select one or more vehicles surrounding the vehicle having a car probe that includes an indicator of unusual behavior. An information entropy is determined from received car probes, for the vehicle having a car probe that includes an indicator of unusual behavior and for each of the selected surrounding vehicles. An anomalous point value is calculated for each vehicle, based on the determined information entropies. In response to determining that the anomalous point value for a car exceeds a pre-determined threshold, the vehicle is flagged as a vehicle exhibiting anomalous behavior.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may identify, by an event agent (EA), an event occurring in a geographic space in which a plurality of mobile objects move. The embodiment may include determining the event is an expected event based on predicting time-series changes of the event handled by the EA. The embodiment may manage the one mobile object based on the expected event.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may identify, by an event agent (EA), an event occurring in a geographic space in which a plurality of mobile objects move. The embodiment may determine the event is an expected event based on predicting time-series changes of the event handled by the EA. The embodiment may manage, by a predictive environment agent (PEA), the expected event.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may manage, by a first computing system, a plurality of mobile objects moving within a geographic space. Managing the plurality of mobile objects may assisting with movement of the plurality of mobile objects. The embodiment may determine whether a first mobile object among the plurality of mobile objects is a real mobile object based on a first sensor information received from the first mobile object. The embodiment may use information received from the first mobile object in managing the plurality of mobile objects moving within the geographic space based on determining that the first mobile object is the real mobile object.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may identify, by an event agent (EA), an event occurring in a geographic space in which a plurality of mobile objects move. The embodiment may determine the event is an expected event based on predicting time-series changes of the event handled by the EA. The embodiment may manage the one mobile object based on the expected event.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may identify, by an event agent (EA), an event occurring in a geographic space in which a plurality of mobile objects move. The embodiment may determine the event is an expected event based on predicting time-series changes of the event handled by the EA. The embodiment may manage, by a predictive environment agent (PEA), the expected event.

Abstract: Phenomenon data for at least one moving phenomenon that affects travel on at least one roadway can be received. An impact distribution for the phenomenon over a plurality of phenomenon cells can be determined and corresponding phenomenon impact distribution data can be generated. For at least one vehicle, a plurality of candidate routes for the vehicle to reach an intended destination can be determined. For each candidate route, whether the candidate route intersects with at least phenomenon cell impacted by the phenomenon can be determined. If so, a vehicle simulation agent for that vehicle and that candidate route can be initialized. Using the vehicle simulation agent, an impact of the phenomenon on the vehicle can be determined based on the phenomenon impact distribution data. Data indicating the impact of the phenomenon on travel of the vehicle if the vehicle were to travel along the respective the route can be output.

Abstract: Phenomenon data for at least one moving phenomenon that affects travel on at least one roadway can be received. An impact distribution for the phenomenon over a plurality of phenomenon cells can be determined and corresponding phenomenon impact distribution data can be generated. For at least one vehicle, a plurality of candidate routes for the vehicle to reach an intended destination can be determined. For each candidate route, whether the candidate route intersects with at least phenomenon cell impacted by the phenomenon can be determined. If so, a vehicle simulation agent for that vehicle and that candidate route can be initialized. Using the vehicle simulation agent, an impact of the phenomenon on the vehicle can be determined based on the phenomenon impact distribution data. Data indicating the impact of the phenomenon on travel of the vehicle if the vehicle were to travel along the respective the route can be output.

Abstract: Event data for at least one moving event is received. From the event data, moving event data indicating a trend of the moving event can be generated. For each of a plurality of vehicles, historical trip pattern data can be accessed and, based on the historical trip pattern data, a probability that the vehicle will be affected by the moving event can be determined. A moving event simulation can be generated based on, at least in part, the historical pattern data and the trend of the moving event. The moving event simulation can predict future locations of the vehicle and the moving event at each of a plurality of future time intervals. Based on the moving event simulation, a determination can be made as to when the vehicle will be affected by the moving event. A notification regarding the moving event can be communicated.

Abstract: Embodiments include determining a condition of category division of a key performance indicator. Aspects include dividing multiple pieces of workflow execution information into a division number for the type of the key performance indicator and generating a graph for each of the multiple groups after the division, from workflow execution information belonging to the group. Aspects also include calculating graph edit distances for respective two arbitrary graphs and generating a new group by merging two graphs having the shortest graph edit distance. Aspects further include calculating a graph edit distance for the new group and repeating the generation of the new group and the calculation of the graph edit distance for the new group, until the graph edit distance is equal to or more than a certain fixed edit distance.

Abstract: Embodiments include determining a condition of category division of a key performance indicator. Aspects include dividing multiple pieces of workflow execution information into a division number for the type of the key performance indicator and generating a graph for each of the multiple groups after the division, from workflow execution information belonging to the group. Aspects also include calculating graph edit distances for respective two arbitrary graphs and generating a new group by merging two graphs having the shortest graph edit distance. Aspects further include calculating a graph edit distance for the new group and repeating the generation of the new group and the calculation of the graph edit distance for the new group, until the graph edit distance is equal to or more than a certain fixed edit distance.

Abstract: Embodiments include determining a condition of category division of a key performance indicator. Aspects include dividing multiple pieces of workflow execution information into a division number for the type of the key performance indicator and generating a graph for each of the multiple groups after the division, from workflow execution information belonging to the group. Aspects also include calculating graph edit distances for respective two arbitrary graphs and generating a new group by merging two graphs having the shortest graph edit distance. Aspects further include calculating a graph edit distance for the new group and repeating the generation of the new group and the calculation of the graph edit distance for the new group, until the graph edit distance is equal to or more than a certain fixed edit distance.