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A small package that enables you to encapsulate and propagate processing/validation results with their corresponding message and status.

A small package that enables you to encapsulate and propagate processing/validation results with their corresponding message and status.

A small package that enables you to encapsulate and propagate processing/validation results with their corresponding message and status.

This library holds a class to handle single live events in Android MVVM architectural pattern. This class is extended form LiveData class, from `androidx.lifecycle:lifecycle-extensions` library, to propagate the data as an event, which means it emits data just once.

This library holds a class to handle single live events in Android MVVM architectural pattern. This class is extended form LiveData class, from `androidx.lifecycle:lifecycle-extensions` library, to propagate the data as an event, which means it emits data just once.

The aim of this library is to allow developers who use spring-batch to propagate information from the main thread that runs the batch to the executions context of the batch items : ItemReader, ItemProcessor and ItemWriter. For example we some times need to extract the current user from Security Context, so instead of writing the code that passes the current user information as a job parameter we let this library to handle it. This library can be extended to support any information developer want to add.

The AppOptics Java agent provides an instrumentation SDK for AWS Lambda as the appoptics-aws-lambda Maven artifact, which provides a convenience wrapper to trace functions, wrappers to trace the operation and propagate the trace context in Apache Http clients, and OpenTelemetry-compatible SDK to create custom spans for additional insight. In addition, appoptics-aws-lambda supports code profiling when the function is configured with enough memory/CPU.

The rooster crows immediately before sunrise, the rooster causes the sun to rise. Using GraphX to detect possible causes and effects between time related events. We observe a true causation signal by generating random correlations over same events at different time and back propagate these scores to their most connected events. Finally, we extract the most probable causes and effects together with a score of aggressiveness (how likely an event could explain downstream effects) and sensitivity (how likely an event results from an upstream cause).