Abstract: An electronic device detects a user input while in a lower energy consumption state. After a latency period, while in a higher energy consumption state, the electronic device delivers to an application a sequence of input events that represent the user input, including, in sequence: a first input event, a second input event and a third input event that represent the user input at first, second and third input times and is delivered at first, second and third delivery times. A time interval between the second input time and the second delivery time is smaller than a time interval between the first input time and the first delivery time, and a time interval between the third input time and the third delivery time is smaller than the time interval between the second input time and the second delivery time.

Abstract: An electronic device, detects a touch input. After a latency period, the device delivers to an application a sequence of input events that represent the touch input, including, in sequence: a first input event that represents the touch input at a first input time and is delivered at a first delivery time, a second input event that represents the touch input at a second input time and is delivered at a second delivery time, and a third input event that represents the touch input at a third input time and is delivered at a third delivery time, a time interval between the second input time and the second delivery time is smaller than a time interval between the first input time and the first delivery time, and a time interval between the third input time and the third delivery time is smaller than the time interval between the second input time and the second delivery time.

Abstract: An electronic device, detects a touch input. After a latency period, the device delivers to an application a sequence of input events that represent the touch input, including, in sequence: a first input event that represents the touch input at a first input time and is delivered at a first delivery time, a second input event that represents the touch input at a second input time and is delivered at a second delivery time, and a third input event that represents the touch input at a third input time and is delivered at a third delivery time, a time interval between the second input time and the second delivery time is smaller than a time interval between the first input time and the first delivery time, and a time interval between the third input time and the third delivery time is smaller than the time interval between the second input time and the second delivery time.

Abstract: Direct access to device functionality from a low power state of the device is provided. The direct access can be provided without an initial user input to wake the device or the display of the device before activating the desired functionality. The low power state of the device may include a low power state of the display in which the time is persistently displayed, and may include a low power state or an inactive state of a main processor of the device. The direct access can be provided by detecting a type, location, or other aspects of a user input received while the device is in the low power state.

Abstract: A system for processing data is provided. During operation, the system obtains a current window of one or more intervals of timeseries data collected from a monitored system. Next, the system continuously performs a statistical hypothesis test that compares the one or more intervals of the time-series data with baseline values from historic time-series data associated with the monitored system. When the statistical hypothesis test indicates a deviation of the time-series data from the baseline values, the system outputs an alert of an anomaly represented by the deviation.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of components of a time-series performance metric associated with an anomaly in a performance of one or more monitored systems. For each component in the set of components, the system performs a statistical hypothesis test on the component to assess a deviation of the component from a baseline value of the component. When the statistical hypothesis test identifies a statistically significant deviation of the component from the baseline value, the system outputs an alert comprising a root cause of the anomaly that is represented by the statistically significant deviation of the component from the baseline value.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a component of a time-series performance metric associated with a server-side root cause of an anomaly in the time-series performance metric. Next, the system obtains a call graph representation of the component, wherein the call graph representation includes a parent node having a parent value of the component and a set of child nodes of the parent node, each child node having a corresponding child value of the component. The system then analyzes the call graph representation to identify one or more of the child nodes as sources of the anomaly. Finally, the system outputs an alert that identifies the sources of the anomaly.

Abstract: Example methods and apparatus to model consumer awareness for changing products in a consumer purchase model are disclosed. A disclosed example method includes receiving utility values associated with at least one of a product or a product attribute, and identifying an agent awareness state associated with the restage product and the original product. The example method also includes calculating a choice probability for the restage product based on the estimated utility values associated with the identified awareness state, and outputting the choice probability for use in a simulation of consumer purchase.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a component of a time-series performance metric associated with a server-side root cause of an anomaly in the time-series performance metric. Next, the system obtains a call graph representation of the component, wherein the call graph representation includes a parent node having a parent value of the component and a set of child nodes of the parent node, each child node having a corresponding child value of the component. The system then analyzes the call graph representation to identify one or more of the child nodes as sources of the anomaly. Finally, the system outputs an alert that identifies the sources of the anomaly.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of components of a time-series performance metric associated with an anomaly in a performance of one or more monitored systems. For each component in the set of components, the system performs a statistical hypothesis test on the component to assess a deviation of the component from a baseline value of the component. When the statistical hypothesis test identifies a statistically significant deviation of the component from the baseline value, the system outputs an alert comprising a root cause of the anomaly that is represented by the statistically significant deviation of the component from the baseline value.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a current window of one or more intervals of time-series data collected from a monitored system. Next, the system continuously performs a statistical hypothesis test that compares the one or more intervals of the time-series data with baseline values from historic time-series data associated with the monitored system. When the statistical hypothesis test indicates a deviation of the time-series data from the baseline values, the system outputs an alert of an anomaly represented by the deviation.

Abstract: Example methods and apparatus to model consumer awareness for changing products in a consumer purchase model are disclosed. A disclosed example method includes receiving utility values associated with at least one of a product or a product attribute, and identifying an agent awareness state associated with the restage product and the original product. The example method also includes calculating a choice probability for the restage product based on the estimated utility values associated with the identified awareness state, and outputting the choice probability for use in a simulation of consumer purchase.