Abstract: Methods and apparatus, including computer program products, implementing and using techniques for identifying a driver of a vehicle. Measurement values representing the movement of a vehicle are received from one or more sensors measuring features relating to the movement of the vehicle. Instantaneous dimensions for measuring driver identification are defined. For each dimension statistical features within a given time frame are calculated and a feature map is built, including the time frame and the statistical features. A set of driving features is extracted from the feature map. The set of extracted driving features is compared with previously extracted sets of driving features to create a set of similarity metrics between two drivers. A classification model is trained based on the similarity metrics and is used with the similarity metrics to determine whether data pertaining to a new trip segment should be associated with a known or unknown driver.

Abstract: An approach for a computer to evaluate a predictive model includes identifying features of training samples in a set of training samples. The approach selects evaluation metrics from a set of evaluation metrics as available metrics using identified features and includes determining recommended metrics using the predictive model, the available metrics, and a predetermined set of user-preferred metrics. The approach applies the predictive model created using the set of training samples to a set of test samples to calculate values of the available metrics. The approach evaluates the predictive model by using the available metrics and the values of the available metrics to evaluate the predictive model by evaluating the predictive model using the recommended metrics and the values of the recommended metrics.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for identifying a driver of a vehicle. Measurement values representing the movement of a vehicle are received from one or more sensors measuring features relating to the movement of the vehicle. Instantaneous dimensions for measuring driver identification are defined. For each dimension statistical features within a given time frame are calculated and a feature map is built, including the time frame and the statistical features. A set of driving features is extracted from the feature map. The set of extracted driving features is compared with previously extracted sets of driving features to create a set of similarity metrics between two drivers. A classification model is trained based on the similarity metrics and is used with the similarity metrics to determine whether data pertaining to a new trip segment should be associated with a known or unknown driver.

Abstract: An embodiment of the invention provides a method that identifies GPS shifting fields for road segments, the GPS shifting fields including areas around the road segments that include false GPS readings of objects that traveled on the road segments. The GPS shifting fields can be revised with a road segment attribute and/or at a driver preference. The road map network can be partitioned into space units, where the road map network can include at least two GPS readings of the object. For each space unit that includes a road segment, a probability that the object was located on the road segment can be calculated for each road segment based on the GPS readings of the object and the GPS shifting fields. The trajectory of the object can be determined based on the computing of the probabilities.

Abstract: An approach for a computer to evaluate a predictive model includes identifying features of training samples in a set of training samples. The approach selects evaluation metrics from a set of evaluation metrics as available metrics using identified features and includes determining recommended metrics using the predictive model, the available metrics, and a predetermined set of user-preferred metrics. The approach applies the predictive model created using the set of training samples to a set of test samples to calculate values of the available metrics. The approach evaluates the predictive model by using the available metrics and the values of the available metrics to evaluate the predictive model by evaluating the predictive model using the recommended metrics and the values of the recommended metrics.

Abstract: An embodiment of the invention provides a method that identifies GPS shifting fields for road segments, the GPS shifting fields including areas around the road segments that include false GPS readings of objects that traveled on the road segments. The GPS shifting fields can be revised with a road segment attribute and/or at a driver preference. The road map network can be partitioned into space units, where the road map network can include at least two GPS readings of the object. For each space unit that includes a road segment, a probability that the object was located on the road segment can be calculated for each road segment based on the GPS readings of the object and the GPS shifting fields. The trajectory of the object can be determined based on the computing of the probabilities.

Abstract: In an approach for evaluating a predictive model, a computer identifies features of training samples in a set of training samples and selects at least one evaluation metric from a set of evaluation metrics as one or more available metrics based on the identified features. The computer applies a predictive model created based on the set of training samples to a set of test samples so as to calculate values of the one or more available metrics and evaluates the predictive model by using the one or more available metrics and the values of the available metrics. With the technical solutions described with respect to the embodiments of the present invention, one or more evaluation metrics that are applicable to specific training sample features may be determined from several evaluation metrics, so that users can precisely evaluate predictive models by using the determined evaluation metrics.

Abstract: A method and an apparatus for generating data in a missing segment of a target time data sequence are disclosed. The method includes: determining whether there is a breakpoint in the missing segment; determining candidate values of the data in the missing segment; and generating values of the data in the missing segment by selectively using the candidate values of the data in the missing segment, according to whether there is the breakpoint in the missing segment. With the method and the apparatus, the data in the missing segment of the target time data sequence can be generated more accurately.

Abstract: A method and an apparatus for generating data in a missing segment of a target time data sequence are disclosed. The method includes: determining whether there is a breakpoint in the missing segment; determining candidate values of the data in the missing segment; and generating values of the data in the missing segment by selectively using the candidate values of the data in the missing segment, according to whether there is the breakpoint in the missing segment. With the method and the apparatus, the data in the missing segment of the target time data sequence can be generated more accurately.

Abstract: Embodiments of the present invention relate to a method and system for automatic event analysis. In particular, according to embodiments of the present invention, there is disclosed a method for automatic event analysis, comprising: collecting information on events of a predetermined type occurring at, within a period of time, at least one location in a space; determining a predictability of the events of the predetermined type with respect to the at least one location based on the collected information and a predetermined condition(s) related to the events of the predetermined type; and determining the stability of predictable events with respect to the at least one location within the period of time. Further, there is disclosed a corresponding system. According to embodiments of the present invention, it is possible to classify an event associated with a service and analyze the service demands in a more efficient way.

Abstract: In an approach for identifying subsurface material layers, a computer processor: acquires a well log of a location to be explored, the log comprising data corresponding to multiple geophysical parameters, the data of each parameter comprising measurement values of the parameter at different depths underground; matches a reference data of each parameter corresponding to each of multiple layer transition types with the data of that parameter in the well log at depths underground, wherein each layer transition type indicates an upper material layer and a lower material layer, and the reference data is used to represent a variation trend of the parameter in a transitional zone conforming with the layer transition type; and according to the matching result, determines a layer transition type at the location to be explored and a depth of an interface between the upper material layer and the lower indicated by the layer transition type.

Abstract: In an approach for evaluating a predictive model, a computer identifies features of training samples in a set of training samples and selects at least one evaluation metric from a set of evaluation metrics as one or more available metrics based on the identified features. The computer applies a predictive model created based on the set of training samples to a set of test samples so as to calculate values of the one or more available metrics and evaluates the predictive model by using the one or more available metrics and the values of the available metrics. With the technical solutions described with respect to the embodiments of the present invention, one or more evaluation metrics that are applicable to specific training sample features may be determined from several evaluation metrics, so that users can precisely evaluate predictive models by using the determined evaluation metrics.

Abstract: Embodiments of the present invention relate to a method and system for automatic event analysis. In particular, according to embodiments of the present invention, there is disclosed a method for automatic event analysis, comprising: collecting information on events of a predetermined type occurring at, within a period of time, at least one location in a space; determining a predictability of the events of the predetermined type with respect to the at least one location based on the collected information and a predetermined condition(s) related to the events of the predetermined type; and determining the stability of predictable events with respect to the at least one location within the period of time. Further, there is disclosed a corresponding system. According to embodiments of the present invention, it is possible to classify an event associated with a service and analyze the service demands in a more efficient way.