Abstract: Raw sensor data from a device of a user is collected. Trips from the sensor data are identified as raw trip data. Metadata for the trips is identified, linked to the trips, and maintained separately from the trip data. The trips are staged, and the corresponding trip data normalized when obtained from a storage or memory location. Normalized trip data is piped or made accessible to feature enhancing applications (apps), each app associating one or more features and events with a given trip. The features and events are maintained for the trips in event and feature level of detail tables. The tables are processed by consuming apps for purposes of updating user-level attributes associated with the user. In an embodiment, custom apps process the raw trip data to add additional features and events, which are directly integrated and updated in the tables for access by the consuming apps.

Abstract: Methods, systems, and computer program products for using a confidence measure to automatically identify a diagnostic analyzer that applies to a diagnostic artifact. A plurality of diagnostic analyzers are each configured to analyze diagnostic artifacts relating to prior executions of software entities. A confidence measure is calculated for each diagnostic analyzer. Each confidence measure indicates a likelihood that the diagnostic analyzer applies to a particular diagnostic artifact. Calculating each confidence measure comprises applying one or more heuristics specific to the diagnostic analyzer against the particular diagnostic artifact, with an outcome of application of each heuristic contributing to the confidence measure for the respective diagnostic analyzer.

Abstract: Methods, systems, and computer program products for using a confidence measure to automatically identify a diagnostic analyzer that applies to a diagnostic artifact. A plurality of diagnostic analyzers are each configured to analyze diagnostic artifacts relating to prior executions of software entities. A confidence measure is calculated for each diagnostic analyzer. Each confidence measure indicates a likelihood that the diagnostic analyzer applies to a particular diagnostic artifact. Calculating each confidence measure comprises applying one or more heuristics specific to the diagnostic analyzer against the particular diagnostic artifact, with an outcome of application of each heuristic contributing to the confidence measure for the respective diagnostic analyzer.