Uploaded image for project: 'Metron (Retired)'
  1. Metron (Retired)
  2. METRON-594

Replay Telemetry Data through Profiler

    XMLWordPrintableJSON

Details

    • Improvement
    • Status: Done
    • Major
    • Resolution: Done
    • None
    • None
    • None

    Description

      The Profiler currently consumes live telemetry, in real-time, as it is streamed through Metron. A useful extension of this functionality would allow the Profiler to also consume archived, historical telemetry. Allowing a user to selectively replay archived, historical raw telemetry through the Profiler has a number of applications. The following use cases help describe why this might be useful.

      Use Case 1 - Model Development
      When developing a new model, I often need a feature set of historical data on which to train my model. I can either wait days, weeks, months for the Profiler to generate this based on live data or I could re-run the raw, historical telemetry through the Profiler to get started immediately. It is much simpler to use the same mechanism to create this historical data set, than a separate batch-driven tool to recreate something that approximates the historical feature set.

      Use Case 2 - Model Deployment
      When deploying an analytical model to a new environment, like production, on day 1 there is often no historical data for the model to work with. This often leaves a gap between when the model is deployed and when that model is actually useful. If I could replay raw telemetry through the profiler a historical feature set could be created as part of the deployment process. This allows my model to start functioning on day 1.

      Use Case 3 - Profile Validation
      When creating a Profile, it is difficult to understand how the configured profile might behave against the entire data set. By creating the profile and watching it consume real-time streaming data, I only have an understanding of how it behaves on that small segment of data. If I am able to replay historical telemetry, I can instantly understand how it behaves on a much larger data set; including all the anomalies and exceptions that exist in all large data sets.

      Attachments

        Issue Links

          Activity

            People

              Unassigned Unassigned
              nickwallen Nick Allen
              Votes:
              0 Vote for this issue
              Watchers:
              2 Start watching this issue

              Dates

                Created:
                Updated:
                Resolved: