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[DOCS] Refreshes population job examples (#36101)
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docs/reference/ml/populations.asciidoc

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@@ -32,33 +32,34 @@ PUT _xpack/ml/anomaly_detectors/population
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{
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"description" : "Population analysis",
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"analysis_config" : {
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"bucket_span":"10m",
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"bucket_span":"15m",
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"influencers": [
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"username"
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"clientip"
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],
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"detectors": [
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{
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"function": "mean",
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"field_name": "bytesSent",
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"over_field_name": "username" <1>
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"field_name": "bytes",
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"over_field_name": "clientip" <1>
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}
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]
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},
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"data_description" : {
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"time_field":"@timestamp",
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"time_field":"timestamp",
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"time_format": "epoch_ms"
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}
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}
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----------------------------------
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//CONSOLE
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// TEST[skip:needs-licence]
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<1> This `over_field_name` property indicates that the metrics for each user (
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as identified by their `username` value) are analyzed relative to other users
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<1> This `over_field_name` property indicates that the metrics for each client (
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as identified by their IP address) are analyzed relative to other clients
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in each bucket.
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If your data is stored in {es}, you can use the population job wizard in {kib}
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to create a job with these same properties. For example, the population job
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wizard provides the following job settings:
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to create a job with these same properties. For example, if you add the sample
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web logs in {kib}, you can use the following job settings in the population job
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wizard:
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[role="screenshot"]
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image::images/ml-population-job.jpg["Job settings in the population job wizard]
@@ -81,6 +82,6 @@ details about the anomalies:
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[role="screenshot"]
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image::images/ml-population-anomaly.jpg["Anomaly details for a specific user"]
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In this example, the user identified as `antonette` sent a high volume of bytes
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on the date and time shown. This event is anomalous because the mean is two times
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higher than the expected behavior of the population.
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In this example, the client IP address `29.64.62.83` received a high volume of
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bytes on the date and time shown. This event is anomalous because the mean is
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three times higher than the expected behavior of the population.

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