Abstract: A system coupled to a packet-based network is configured to predict the locations of one or more mobile devices communicating with the packet-based network. The system comprises a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The system further comprises an off-line prediction subsystem configured to train a plurality of off-line prediction models and an on-line prediction model using various historical location events. The off-line prediction subsystem is further configured to generate off-line prediction results corresponding to the off-line prediction models.

Abstract: A system coupled to a packet-based network is configured to predict the locations of mobile devices that have communicated with the packet-based network. The system includes a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The geo-places include geo-blocks and geo-fences. The system further comprises a location prediction subsystem configured to construct first feature space using first location events and second feature space using second location events, and to extract a set of labels from third location events. The location prediction subsystem is further configured to train a prediction model using the first feature space and the set of labels, and to apply the prediction model to the second feature space to obtain prediction results.

Abstract: The present disclosure provides methods and systems that utilize mobile device location events and machine learning and generate predicative classification/regression model for lookalike prediction. Location related features, together with other user level information, are extracted, transformed and used as model feature input, and a client specified list of mobile devices or their associated users are used as prediction target. This system makes efficient use of different types of location events and thus offers improved scale and performance. It also enjoys many benefits offered by a machine learning platform, such as automatic adaptation to different lists of seed lists, addition of new features and changes in data statistical properties.

Abstract: A system coupled to a packet-based network is configured to predict the locations of mobile devices that have communicated with the packet-based network. The system includes a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The geo-places include geo-blocks and geo-fences. The system further comprises a location prediction subsystem configured to construct first feature space using first location events and second feature space using second location events, and to extract a set of labels from third location events. The location prediction subsystem is further configured to train a prediction model using the first feature space and the set of labels, and to apply the prediction model to the second feature space to obtain prediction results.

Abstract: System and methods for running a location-based information campaign (campaign) select one or more first geo-blocks to form a first geo-fence from a plurality of geo-blocks each corresponding to a geographical region having at least one border defined by a real-world object and overlapping substantially with a targeted region associated with the location-based information campaign, and process information requests with respect to the first geo-fence. Each of the one or more first geo-blocks is associated with a respective performance score above a first threshold. The system and methods further monitor a pacing status associated with the campaign, and in response to a pacing goal associated with the campaign not being met, define a second geo-fence for the campaign, the second geo-fence including the one or more first geo-blocks and one or more second geo-blocks each associated with a respective performance score above a second threshold that is below the first threshold.

Abstract: The present disclosure provides novel techniques to derive characteristic of mobile entities based signals from mobile devices associated with these mobile entities. In certain embodiments, a data file is built for each mobile entity to transform originally extremely high dimensional raw data derived from mobile devices communicating with a network into structured data characterizing associated mobile entities. The data file are structured for filtering and searching with respect to location and non-location features. A plurality of filters for a behavioral segment are applied to the data files of a large number of mobile entities. The plurality of filters including a preset hard constraint (HC) filter, a preset soft constraint (SC) filter, and an extended constraint (EC) filter built using location features in data files that have passed through the HC filter and/or the SC filter.

Abstract: The present disclosure provides method and apparatus for measuring effect of information delivered to mobile devices. In certain embodiments, a method performed by one or more computer systems coupled to a packet-based network comprises receiving a first plurality of request data packets via the packet-based network, receiving panel data packets via the packet-based network, and selecting a set of calibration mobile devices from the first plurality of mobile devices, each of the set of calibration mobile device having transmitted at least one of the panel data packets. The calibration mobile devices are used to derive a calibration factor.

Abstract: System and methods for running a location-based information campaign (campaign) select one or more first geo-blocks to form a first geo-fence from a plurality of geo-blocks each corresponding to a geographical region having at least one border defined by a real-world object and overlapping substantially with a targeted region associated with the location-based information campaign, and process information requests with respect to the first geo-fence. Each of the one or more first geo-blocks is associated with a respective performance score above a first threshold. The system and methods further monitor a pacing status associated with the campaign, and in response to a pacing goal associated with the campaign not being met, define a second geo-fence for the campaign, the second geo-fence including the one or more first geo-blocks and one or more second geo-blocks each associated with a respective performance score above a second threshold that is below the first threshold.

Abstract: An adaptive automatic change management for computer system landscapes is described herein. A predefined set of attributes are extracted or obtained for a computer system artifact, together with a number of values assigned to the set of attributes. A unique identification section is created and distributed among the computer systems in the landscape based on the set of attributes and the assigned values, where the unique identification section encompass computer system artifacts of a same kind. A modification of an artifact of the same kind is tracked at a computer system. The tracked modification is automatically applied in the computer system to one or more incoming computer system artifacts of the same kind.

Abstract: An adaptive automatic change management for computer system landscapes is described herein. A predefined set of attributes are extracted or obtained for a computer system artifact, together with a number of values assigned to the set of attributes. A unique identification section is created and distributed among the computer systems in the landscape based on the set of attributes and the assigned values, where the unique identification section encompass computer system artifacts of a same kind. A modification of an artifact of the same kind is tracked at a computer system. The tracked modification is automatically applied in the computer system to one or more incoming computer system artifacts of the same kind.