From 527b789e6fdbaead0479da406daafb0e9b84234e Mon Sep 17 00:00:00 2001 From: Lisa Cawley Date: Fri, 23 Jun 2017 10:53:16 -0700 Subject: [PATCH] [DOCS] Add configuration information for population analysis (elastic/x-pack-elasticsearch#1653) * [DOCS] Add configuration information for population analysis * [DOCS] Add ML population analysis examples * [DOCS] Address feedback for population analysis * [DOCS] More feedback on population analysis Original commit: elastic/x-pack-elasticsearch@ffa2bfeed9ee06a0c6df7820afec6edab773c29c --- docs/en/ml/configuring.asciidoc | 1 + docs/en/ml/images/ml-population-anomaly.jpg | Bin 0 -> 155371 bytes docs/en/ml/images/ml-population-job.jpg | Bin 0 -> 62938 bytes docs/en/ml/images/ml-population-results.jpg | Bin 0 -> 219816 bytes docs/en/ml/populations.asciidoc | 87 ++++++++++++++++++++ 5 files changed, 88 insertions(+) create mode 100644 docs/en/ml/images/ml-population-anomaly.jpg create mode 100644 docs/en/ml/images/ml-population-job.jpg create mode 100644 docs/en/ml/images/ml-population-results.jpg create mode 100644 docs/en/ml/populations.asciidoc diff --git a/docs/en/ml/configuring.asciidoc b/docs/en/ml/configuring.asciidoc index 9004f2cab63..e5c7a8858ac 100644 --- a/docs/en/ml/configuring.asciidoc +++ b/docs/en/ml/configuring.asciidoc @@ -33,3 +33,4 @@ The scenarios in this section describe some best practices for generating useful include::aggregations.asciidoc[] include::categories.asciidoc[] +include::populations.asciidoc[] diff --git a/docs/en/ml/images/ml-population-anomaly.jpg b/docs/en/ml/images/ml-population-anomaly.jpg new file mode 100644 index 0000000000000000000000000000000000000000..cde94e8c8d5595b5021ab55d75554a66eb1d8f88 GIT binary patch literal 155371 zcmeEucUV(RoA(K!C`GDt2q;PuqEe-*2nYxPK@boS5h1itB}gDhucDx!ROup3se(YH zD=584kdh$1B`OdhBwzH|$9H%C`gY&%`mVip_i)WQzuaeL&fIfm=G<+dex}X=9EN%Z zdH@In0GDZh02K$E*G0nb0D!SEAOirvApjHu2k2>tmIV-`B>Et7Y&T*f7L)ce&Z9rA;+jl;$$*VO}`R$_~|<7Vki~Q 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or +* Their behavior is different than other entities in a specified population. + +The latter method of detecting outliers is known as _population analysis_. The +{ml} analytics build a profile of what a "typical" user, machine, or other entity +does over a specified time period and then identify when one is behaving +abnormally compared to the population. + +This type of analysis is most useful when the behavior of the population as a +whole is mostly homogeneous and you want to identify outliers. In general, +population analysis is not useful when members of the population inherently +have vastly different behavior. You can, however, segment your data into groups +that behave similarly and run these as separate jobs. For example, you can use a +query filter in the {dfeed} to segment your data or you can use the +`partition_field_name` to split the analysis for the different groups. + +Population analysis scales well and has a lower resource footprint than +individual analysis of each series. For example, you can analyze populations +of hundreds of thousands or millions of entities. + +To specify the population, use the `over_field_name` property. For example: + +[source,js] +---------------------------------- +PUT _xpack/ml/anomaly_detectors/population +{ + "description" : "Population analysis", + "analysis_config" : { + "bucket_span":"10m", + "influencers": [ + "username" + ], + "detectors": [ + { + "function": "mean", + "field_name": "bytesSent", + "over_field_name": "username" <1> + } + ] + }, + "data_description" : { + "time_field":"@timestamp", + "time_format": "epoch_ms" + } +} +---------------------------------- +//CONSOLE +<1> This `over_field-name` property indicates that the metrics for each user ( + as identified by their `username` value) are analyzed relative to other users + in each bucket. + +//TO-DO: Per sophiec20 "Perhaps add the datafeed config and add a query filter to +//include only workstations as servers and printers would behave differently +//from the population + +If your data is stored in {es}, you can create an advanced job with these same +properties. In particular, you specify the `over_field_name` property when you +add detectors: + +[role="screenshot"] +image::images/ml-population-job.jpg["Create a detector for population analysis] + +After you open the job and start the {dfeed} or supply data to the job, you can +view the results in {kib}. For example: + +[role="screenshot"] +image::images/ml-population-results.jpg["Population analysis results in the Anomaly Explorer"] + +As in this case, the results are often quite sparse. There might be just a few +data points for the selected time period. Population analysis is particularly +useful when you have many entities and the data for specific entitles is sporadic +or sparse. + +If you click on a section in the time line or swim lanes, you can see more +details about the anomalies: + +[role="screenshot"] +image::images/ml-population-anomaly.jpg["Anomaly details for a specific user"] + +In this example, the user identified as `antonette` sent a high volume of bytes +on the date and time shown. This event is anomalous because the mean is two times +higher than the expected behavior of the population.