Abstract: A method and an apparatus for determining a location of a mobile device. The location of a mobile device is determined accurately according to information which includes call data records of the mobile device. By employing a partial ellipse integral model, two physical world factors are taken into consideration in reducing the location uncertainty in call data records. The factors include: spatiotemporal constraints of the device's movement in the physical world and the telecommunication cell area's geometry information, which increase the accuracy of determining the location of a mobile device.

Abstract: A vehicle domain multi-level parallel buffering and context-based streaming data pre-processing system includes a first data processing level and a second data processing level. The first data processing level includes a first-level buffer configured to buffer data provided from a plurality of raw data streams output from a plurality of vehicles. The second data processing level includes an electronic task-queue-dictionary (TQD) module and a plurality of second-level data processing buffers. The TQD module is configured to create a plurality of tasks in response to receiving a serial data stream output from the first-level buffer. The TQD module is further configured to assign each task to a corresponding second-level buffer, and separate the serial data stream into individual data values that are delivered to a specific second-level buffer based on the task so as to generate a multi-level parallel context-based buffering operation.

Abstract: A method of tagging a geographical area includes obtaining, with a processing device, attribute information and mobile tracking data of a plurality of mobile objects, wherein the mobile tracking data comprises sampling time and corresponding sampling point locations of the mobile objects; converting the mobile tracking data of the plurality of mobile objects into new mobile tracking data according to the correspondence relationship between the sampling time and a time slices, wherein the new mobile tracking data include time slices and corresponding sampling point locations; and obtaining a set of attribute information of at least one geographical area with respect to the time slices based on the new mobile tracking data, wherein the at least one geographical area is obtained by clustering the sampling point locations.

Abstract: In one embodiment, a computer-implemented method includes receiving a global positioning system (GPS) location of a mobile device. Two or more road locations are determined as possible locations corresponding to the GPS location in a physical network of a set of roads. A first portion of a virtual network is built, by a computer processor, by expanding the virtual network from a node representing a prior road location to the two or more candidate road locations. A first candidate road location is selected as a current road location from among the two or more candidate road locations. A route of the mobile device is identified as including the prior road location and the first candidate road location. At least one of the two or more candidate road locations not selected as the current road location is excluded from future building of the virtual network. The virtual network is built dynamically.

Abstract: A mechanism is provided in a data processing system for distributed tree learning. A source processing instance distributes data record instances to a plurality of model update processing items. The plurality of model update processing items determine candidate leaf splitting actions in a decision tree in parallel based on the data record instances. The plurality of model update processing items send the candidate leaf splitting actions to a plurality of conflict resolve processing items. The plurality of conflict resolve processing items identifies conflict leaf splitting actions. The plurality of conflict resolve processing items applies tree structure changes to the decision tree in the plurality of model update processing items.

Abstract: A mechanism is provided in a data processing system for distributed tree learning. A source processing instance distributes data record instances to a plurality of model update processing items. The plurality of model update processing items determine candidate leaf splitting actions in a decision tree in parallel based on the data record instances. The plurality of model update processing items send the candidate leaf splitting actions to a plurality of conflict resolve processing items. The plurality of conflict resolve processing items identifies conflict leaf splitting actions. The plurality of conflict resolve processing items applies tree structure changes to the decision tree in the plurality of model update processing items.

Abstract: A method and apparatus of location sequence inferences for moving objects traveling along a path. The method and apparatus primarily concerns determining the location of a moving vehicle on a roadway in a roadway network. The inputs to the system include: raw GPS tracking sequence with timestamp, trajectory of the moving object inferred by map matching, accurate speed sequence from a reliable device, e.g. OBD (On-Board Diagnostics is an automotive term referring to a vehicle's self-diagnostic and reporting capability), historical map matching results and historical locations sequence inference results. The output of the system is a sequence of more accurate location (on road segments) sequences than raw GPS locations and map matching results.

Abstract: A method of tagging a geographical area includes obtaining, with a processing device, attribute information and mobile tracking data of a plurality of mobile objects, wherein the mobile tracking data comprises sampling time and corresponding sampling point locations of the mobile objects; converting the mobile tracking data of the plurality of mobile objects into new mobile tracking data according to the correspondence relationship between the sampling time and a time slices, wherein the new mobile tracking data include time slices and corresponding sampling point locations; and obtaining a set of attribute information of at least one geographical area with respect to the time slices based on the new mobile tracking data, wherein the at least one geographical area is obtained by clustering the sampling point locations.

Abstract: A method of tagging a geographical area includes obtaining, with a processing device, attribute information and mobile tracking data of a plurality of mobile objects, wherein the mobile tracking data comprises sampling time and corresponding sampling point locations of the mobile objects; converting the mobile tracking data of the plurality of mobile objects into new mobile tracking data according to the correspondence relationship between the sampling time and a time slices, wherein the new mobile tracking data include time slices and corresponding sampling point locations; and obtaining a set of attribute information of at least one geographical area with respect to the time slices based on the new mobile tracking data, wherein the at least one geographical area is obtained by clustering the sampling point locations.

Abstract: A method for recognizing a primitive in an image includes recognizing at least one primitive in the image to obtain at least one candidate shape of the at least one primitive, which at least one candidate shape has a respective confidence; determining whether the recognizing of the at least one primitive has a potential error based on the confidence; obtaining auxiliary information about the at least one primitive from a user in response to determining that the recognizing has the potential error; and re-recognizing the at least one primitive at least in part based on the auxiliary information.

Abstract: A method for recognizing a primitive in an image includes recognizing at least one primitive in the image to obtain at least one candidate shape of the at least one primitive, which at least one candidate shape has a respective confidence; determining whether the recognizing of the at least one primitive has a potential error based on the confidence; obtaining auxiliary information about the at least one primitive from a user in response to determining that the recognizing has the potential error; and re-recognizing the at least one primitive at least in part based on the auxiliary information.

Abstract: A method and system to identify a time lagged indicator of an event to be predicted are described. The method includes receiving information including an indication of a factor, the factor being a different event than the event to be predicted, and identifying a window period within which the event is statistically correlated with the factor. The method also includes collecting data for a duration of the window period, the data indicating occurrences of the factor and the event, and identifying a time lagged dependency of the event on the factor based on analyzing the data.

Abstract: A method and system to perform spatio-temporal prediction are described. The method includes obtaining, based on communication with one or more sources, multi-scale spatial datasets, each of the multi-scale spatial datasets providing a type of information at a corresponding granularity, at least two of the multi-scale spatial datasets providing at least two types of information at different corresponding granularities. The method also includes generating new features for each of the multi-scale spatial datasets, the new features being based on features of each of the multi-scale spatial datasets and spatial relationships between and within the multi-scale spatial datasets. The method further includes selecting, using the processor, features of interest from among the new features, training a predictive model based on the features of interest, and predicting an event based on the predictive model.

Abstract: A method and an apparatus for determining a location of a mobile device. The location of a mobile device is determined accurately according to information which includes call data records of the mobile device. By employing a partial ellipse integral model, two physical world factors are taken into consideration in reducing the location uncertainty in call data records. The factors include: spatiotemporal constraints of the device's movement in the physical world and the telecommunication cell area's geometry information, which increase the accuracy of determining the location of a mobile device.

Abstract: A method of tagging a geographical area includes obtaining, with a processing device, attribute information and mobile tracking data of a plurality of mobile objects, wherein the mobile tracking data comprises sampling time and corresponding sampling point locations of the mobile objects; converting the mobile tracking data of the plurality of mobile objects into new mobile tracking data according to the correspondence relationship between the sampling time and a time slices, wherein the new mobile tracking data include time slices and corresponding sampling point locations; and obtaining a set of attribute information of at least one geographical area with respect to the time slices based on the new mobile tracking data, wherein the at least one geographical area is obtained by clustering the sampling point locations.

Abstract: A method of tagging a geographical area includes obtaining, with a processing device, attribute information and mobile tracking data of a plurality of mobile objects, wherein the mobile tracking data comprises sampling time and corresponding sampling point locations of the mobile objects; converting the mobile tracking data of the plurality of mobile objects into new mobile tracking data according to the correspondence relationship between the sampling time and a time slices, wherein the new mobile tracking data include time slices and corresponding sampling point locations; and obtaining a set of attribute information of at least one geographical area with respect to the time slices based on the new mobile tracking data, wherein the at least one geographical area is obtained by clustering the sampling point locations.

Abstract: A method for recognizing a primitive in an image includes recognizing at least one primitive in the image to obtain at least one candidate shape of the at least one primitive, which at least one candidate shape has a respective confidence; determining whether the recognizing of the at least one primitive has a potential error based on the confidence; obtaining auxiliary information about the at least one primitive from a user in response to determining that the recognizing has the potential error; and re-recognizing the at least one primitive at least in part based on the auxiliary information.

Abstract: A method for recognizing a primitive in an image includes recognizing at least one primitive in the image to obtain at least one candidate shape of the at least one primitive, which at least one candidate shape has a respective confidence; determining whether the recognizing of the at least one primitive has a potential error based on the confidence; obtaining auxiliary information about the at least one primitive from a user in response to determining that the recognizing has the potential error; and re-recognizing the at least one primitive at least in part based on the auxiliary information.