Abstract: In some implementations, a computing device can manage conflicts using conflict islands. For example, when a computing device receives bulk map data updates, the computing device can determine conflicts between point updates (e.g., small, ad hoc feature edits) in the current map data and the bulk map data updates. When the bulk map data updates are merged with the current map data, a point update for a map feature may conflict with the bulk update for the feature. The computing device can determine a perimeter of stable map features around the conflicted feature. The map features within the perimeter can correspond to a conflict island. The bulk updates for features within a conflict island (e.g., within the perimeter) can be deferred until resolved while bulk updates for features outside of a conflict island can be applied to the current map data.

Abstract: Example implementations include a method, apparatus, and computer-readable medium comprising determining, by a processor of a control panel, that a security event has happened; and capturing still images or videos by a camera in the control panel subsequent to determining that the security event has happened. In some implementations, the camera is a forward-facing camera. In some implementations, determining that the security event has happened comprises detecting a motion using the camera in the control panel. In some implementations, determining that the security event has happened comprises receiving a signal indicative of activation of a door switch of a door located next to the control panel. In some implementations, the security event is associated with one or more Bluetooth devices being in range, and determining that the security event has happened comprises using a Bluetooth radio in the control panel to detect the Bluetooth devices.

Abstract: In some implementations, a computing device can manage conflicts using conflict islands. For example, when a computing device receives bulk map data updates, the computing device can determine conflicts between point updates (e.g., small, ad hoc feature edits) in the current map data and the bulk map data updates. When the bulk map data updates are merged with the current map data, a point update for a map feature may conflict with the bulk update for the feature. The computing device can determine a perimeter of stable map features around the conflicted feature. The map features within the perimeter can correspond to a conflict island. The bulk updates for features within a conflict island (e.g., within the perimeter) can be deferred until resolved while bulk updates for features outside of a conflict island can be applied to the current map data.

Abstract: In some implementations, a computing device can manage conflicts using conflict islands. For example, when a computing device receives bulk map data updates, the computing device can determine conflicts between point updates (e.g., small, ad hoc feature edits) in the current map data and the bulk map data updates. When the bulk map data updates are merged with the current map data, a point update for a map feature may conflict with the bulk update for the feature. The computing device can determine a perimeter of stable map features around the conflicted feature. The map features within the perimeter can correspond to a conflict island. The bulk updates for features within a conflict island (e.g., within the perimeter) can be deferred until resolved while bulk updates for features outside of a conflict island can be applied to the current map data.

Abstract: A source device includes various sensors, such as a GPS receiver. The source device provides operation context data to a server that indicates movement of the source device. The source device may also provide a movement context. The server also evaluates usage of data from the device by other applications or devices to determine a usage context. Based on the movement, operation, and usage contexts, the server selects a frequency at which data is collected for a sensor and a frequency at which the data is transmitted to the server. For example, where the device is not moving or is indoors, less location data is collected. Where no user is tracking the device, location data may be transmitted less frequently and may also be collected less frequently.

Abstract: In some implementations, a computing device can manage conflicts using conflict islands. For example, when a computing device receives bulk map data updates, the computing device can determine conflicts between point updates (e.g., small, ad hoc feature edits) in the current map data and the bulk map data updates. When the bulk map data updates are merged with the current map data, a point update for a map feature may conflict with the bulk update for the feature. The computing device can determine a perimeter of stable map features around the conflicted feature. The map features within the perimeter can correspond to a conflict island. The bulk updates for features within a conflict island (e.g., within the perimeter) can be deferred until resolved while bulk updates for features outside of a conflict island can be applied to the current map data.

Abstract: A source device includes various sensors, such as a GPS receiver. The source device provides operation context data to a server that indicates movement of the source device. The source device may also provide a movement context. The server also evaluates usage of data from the device by other applications or devices to determine a usage context. Based on the movement, operation, and usage contexts, the server selects a frequency at which data is collected for a sensor and a frequency at which the data is transmitted to the server. For example, where the device is not moving or is indoors, less location data is collected. Where no user is tracking the device, location data may be transmitted less frequently and may also be collected less frequently.

Abstract: The current document is directed to methods and systems for estimating values that could be derived from a large data set, were it available, from values computed from an available smaller data set. A specific example of the currently described methods and systems are methods and systems that estimate various medical-record-related statistics and values computed from hypothetical datasets. In order to extrapolate the desired statistics and computed values from the observed smaller data set, multiple models are employed by the currently disclosed methods and systems. These models can be employed sequentially to generate relatively fine-grained estimates over various multi-dimensional data-set volumes.

Abstract: The present document is directed to an automated experimentation platform that provides a visual integrated development environment (“IDE”) that allows a user to construct and execute various types of data-driven workflows. The automated experimentation platform includes back-end components that include API servers, a catalog, a cluster-management component, and execution-cluster nodes. Workflows are visually represents as directed acyclic graphs and texturally encoded. The workflows are transformed into jobs that are distributed for execution to the execution-cluster nodes.

Abstract: The current document is directed to methods and automated systems that assign individual medical codes selected from one or more medical codebooks to electronic medical records. In certain implementations, the currently disclosed automated systems generate multiple streams of medical terms or medical terms and phrases from an electronic medical record as well as multiple streams of medical terms or medical terms and phrases from individual medical codes contained within a medical codebook and then use stream-comparison functionality to select those individual medical codes of the medical codebook most likely to be relevant and related to the information encoded within the electronic medical record.