Abstract: A device receives user information associated with a user of a user device that is associated with a network, and creates a user profile, associated with the user, based on the user information. The device determines threats to the network, by the user, based on the user profile. The threats to the network include insider threats, advanced persistent threats, bring your own device (BYOD) threats, cloud security threats, malware threats, and/or denial of service (DoS) threats. The device stores or presents, for display, information associated with the determined threats to the network by the user.

Abstract: A device receives user information associated with users of user devices of a network, and receives marketing information associated with products or services. The device generates user profiles, associated with the users, based on the user information, and receives information relating to a third party user. The third party user is associated with a third party user device, the third party user device is associated with a third party network, and the third party network is different than the network. The device determines, based on the information relating to the third party user and the user profiles, a third party user profile for the third party user, and determines an advertisement to provide to the third party user device based on the third party user profile and the marketing information. The device causes the advertisement to be provided to the third party user device, via the third party network.

Abstract: A device receives information, associated with civil structures, that includes temperature information associated with the civil structures, pressure information associated with the civil structures, stress information associated with the civil structures, vibration information associated with the civil structures, or displacement information associated with the civil structures. The device performs an analysis of the information associated with the civil structures via one or more analytics techniques, and generates analysis information based on the analysis of the information associated with the civil structures. The analysis information identifies a potential issue with at least one of the civil structures, and the device provides the analysis information for display.

Abstract: A device receives healthcare information, associated with users, that includes information associated with a health of the users, and information associated with monitoring devices that monitor the health of the users, or information associated with network connectivity of the monitoring devices. The device performs an analysis of the healthcare information via one or more analytics techniques, and generates analysis information based on the analysis of the healthcare information. The analysis information identifies a potential issue with at least one of the users or at least one of the monitoring devices. The device provides the analysis information for display.

Abstract: A device receives monitored information, associated with multiple users, that includes information indicating multiple environmental events associated with locations of the multiple users, information associated with multiple user devices that monitor the multiple environmental events, and/or information associated with network connectivity of the multiple user devices. The device performs an analysis of the monitored information via analytics techniques, and generates analysis information based on the analysis of the monitored information. The analysis information identifies an environmental event of the multiple environmental events. The device generates a notification concerning the environmental event, and provides the notification to notify a user, of the multiple users, of the environmental event.

Abstract: A device receives a request for a flight path from a first location to a second location in a region, and calculates the flight path based on the request and based on one or more of weather information, air traffic information, obstacle information, regulatory information, or historical information associated with the region. The device determines required capabilities for the flight path based on the request, and selects, from multiple UAVs, a particular UAV based on the required capabilities for the flight path and based on a ranking of the multiple UAVs. The device generates flight path instructions for the flight path, and provides the flight path instructions to the particular UAV to permit the particular UAV to travel from the first location to the second location via the flight path.

Abstract: A device receives a request for a flight path of UAV from a first location to a second location in a region, and determines, based on credentials associated with the UAV, whether the UAV is authenticated for utilizing the device and a network. The device determines, when the UAV is authenticated, capability information for the UAV based on the request and component information associated with the UAV. The device calculates the flight path from the first location to the second location based on the capability information and one or more of weather information, air traffic information, obstacle information, or regulatory information associated with the region. The device generates flight path instructions for the flight path based on one or more of the weather information, the air traffic information, the obstacle information, or the regulatory information associated with the region, and provides the flight path instructions to the UAV.

Abstract: A device receives aviation information associated with aviation in a geographical region, and receives configurability options associated with the aviation information. The device analyzes the aviation information based on the configurability options to generate analyzed information, and receives a request for a flight path for a UAV to travel from a first location to a second location in the geographical region. The device calculates the flight path from the first location to the second location based on the analyzed information and capability information associated with the UAV, and generates flight path instructions for the flight path. The device provides the flight path instructions to the UAV to permit the UAV to travel from the first location to the second location via the flight path.

Abstract: A device receives a request for a flight path for a UAV to travel from a first location to a second location, and determines capability information for the UAV based on component information of the UAV. The device calculates the flight path based on the capability information, identifies multiple recharging stations located on or near the flight path, and selects a recharging station from the multiple recharging stations based on one or more factors. The device generates flight path instructions, for the flight path, that instruct the UAV to stop and recharge at the recharging station. The device provides the flight path instructions to the UAV to permit the UAV to travel from the first location to a location of the recharging station, stop and recharge at the recharging station, and travel from the location of the recharging station to the second location via the flight path.

Abstract: A device receives a request for a flight path from a first location to a second location in a region. The request includes component information associated with components of UAVs in a group. The device calculates a most efficient flight path from the first location to the second location based weather information, air traffic information, obstacle information, or regulatory information associated with the region, and determines capability information for the UAVs in the group based on the component information. The device selects, from the UAVs in the group, a particular UAV that is capable of traversing the most efficient flight path based on the capability information, and generates flight path instructions for the most efficient flight path. The device provides the flight path instructions to the particular UAV to permit the particular UAV to travel from the first location to the second location via the most efficient flight path.

Abstract: A device receives web data, associated with user devices, that is generated based on interactions of the user devices with a network and one or more content provider devices. The device removes erroneous or objectionable web data from the web data to generate a subset of the web data, and categorizes the subset of the web data by assigning categories to the subset of the web data. The device performs an empirical estimation of the categorized subset of the web data to generate empirical estimations. The device performs a simulation of the empirical estimations to generate synthetic data that corresponds to the web data and removes private information relating to the user devices and users of the user devices, and stores the synthetic data in a storage device.

Abstract: A device receives information associated with machine-to-machine (M2M) devices connected to a host server device via a network. The information associated with the M2M devices include one or more of device information associated with components of the M2M devices, application information generated by the M2M devices, or network information associated with interactions of the M2M devices, with the network, when the M2M devices provide the application information to the host server device via the network. The device performs an analysis of the information associated with the M2M devices via one or more analytics techniques, and generates analysis information based on the analysis of the information associated with the M2M devices. The device provides the analysis information for display by the host server device.

Abstract: A user device provides, to a server device, event application setup information associated with the user device. The event application setup information includes information provided to the user device during configuration of an event application. The user device provides, to the server device, user device information associated with the user device. The user device information includes information identifying a current location of the user device, and is provided to the server device during an event that affects the user device. The user device receives, from the server device, custom event instructions based on the event application setup information, the user device information, and the event. The custom event instructions include information describing a recommended route, away from a location of the event, that is customized for the user device. The user displays the custom event instructions.

Abstract: A method and system including software makes predictions about potential business locations. The method includes the steps of providing heterogeneous data including a spatial component, extracting entities from the heterogeneous data, clustering the entities, and making a prediction about the business location using the clustered entities. Entities preferably include stores, people, or other physical objects and each has attributes. Preferably the entities include at least one of the following attributes: Value Ratio, Focal Values, Impact, Revenue Difference, Support, and Baseline Value. In this way, predictions made about the business locations will account for the nature of the business and enable comparison of one business location to another.

Abstract: A method and system for making predictions about business locations. The method includes providing a spatial map and analyzing heterogeneous data having a spatial component to find utilizable data. Relationships are automatically extracted from the utilizable data. The step of automatically extracting relationships includes generating a composite indicator, which correlates spatial data with unstructured data. The extracted relationships are presented on a spatial map to make a prediction about at least one business location. Preferably, the predictions are presented as a rank-ordered list on the spatial map and a heat map overlays the spatial map to indicate predictions about particular regions.

Abstract: A method and system that utilizes OLAP and supporting data structures for making predictions about business locations. The method includes providing a spatial map and analyzing heterogeneous data having a spatial component to find utilizable data. Relationships are automatically extracted from the utilizable data by employing machine learning. The step of automatically extracting relationships includes generating a composite indicator, which correlates spatial data with unstructured data. The extracted relationships are presented on a spatial map to make a prediction about at least one business location. Preferably, the predictions are presented as a rank-ordered list on the spatial map and a heat map overlays the spatial map to indicate predictions about particular regions.

Abstract: A method and system including software makes predictions about potential business locations. The method includes the steps of providing heterogeneous data including a spatial component, extracting entities from the heterogeneous data, clustering the entities, and making a prediction about the business location using the clustered entities. Entities preferably include stores, people, or other physical objects and each has attributes. Preferably the entities include at least one of the following attributes: Value Ratio, Focal Values, Impact, Revenue Difference, Support, and Baseline Value. In this way, predictions made about the business locations will account for the nature of the business and enable comparison of one business location to another.

Abstract: A method and system that utilizes OLAP and supporting data structures for making predictions about business locations. The method includes providing a spatial map and analyzing heterogeneous data having a spatial component to find utilizable data. Relationships are automatically extracted from the utilizable data by employing machine learning. The step of automatically extracting relationships includes generating a composite indicator, which correlates spatial data with unstructured data. The extracted relationships are presented on a spatial map to make a prediction about at least one business location. Preferably, the predictions are presented as a rank-ordered list on the spatial map and a heat map overlays the spatial map to indicate predictions about particular regions.

Abstract: A method and system for making predictions about business locations. The method includes providing a spatial map and analyzing heterogeneous data having a spatial component to find utilizable data. Relationships are automatically extracted from the utilizable data. The step of automatically extracting relationships includes generating a composite indicator, which correlates spatial data with unstructured data. The extracted relationships are presented on a spatial map to make a prediction about at least one business location. Preferably, the predictions are presented as a rank-ordered list on the spatial map and a heat map overlays the spatial map to indicate predictions about particular regions.

Abstract: A method for predicting extreme changes in numeric time series data includes converting a numeric time series into a sequence of symbols. A prediction method, such as a neural network or nearest neighbor algorithm is used to make the forecast. A numeric time series data is identified with extreme changes in them, and a window of length W that precedes the extreme change is extracted. Those extracts of a time series are built into a matrix (characteristic matrix) for singular value decomposition. The built matrix undergoes singular value decomposition, which reveals the characteristic vectors (symbols) that are indicative of time series that have characteristics that precede an extreme event. To perform forecasting, a window of length W in a new time series is generated and the dot product of the windows is taken against a predetermined number of columns of characteristic matrix, and, forecasting is performed on the new series.