Share data in the Splunk App for Anomaly Detection
What data is collected
The Splunk App for Anomaly Detection collects the following basic usage information:
Component | Description | Example |
---|---|---|
app.session.schedule_clicked
|
Information entered in the "Schedule" modal in the Job Dashboard. |
|
app.session.manage_alert_clicked
|
Information entered in the "Manage Alert" modal in the Job Dashboard. |
|
app.session.app_go_to_tab
|
The tab ("Job Dashboard" or "Create a New Job") to which the user changed. |
|
app.session.field_selected
|
Whether the user selected a field for running anomaly detection. |
|
app.session.alert_trigger_saved
|
The information that evaluates the detected anomalies against the alerting conditions to determine whether or not an email should be sent. |
|
app.session.new_job_go_to_tab
|
The tab ("Job Dashboard" or "Create a New Job") to which the user changed. |
|
app.session.schedule_saved
|
The scheduling details that the user entered for the Job execution. |
|
app.session.new_job_saved
|
Name and description of job created by user. |
|
app.session.delete_job_clicked
|
Informs us that the user deleted a job. |
|
app.session.detect_anomalies_clicked
|
Informs us that the user clicked on the "Detect Anomalies" button to initiate anomaly detection. |
|
app.session.sensitivity_saved
|
Informs us of the sensitivity value (low, medium, or high) selected by the user upon operationalization of the AD search. |
|
app.session.create_job_open_in_search_clicked
|
nforms us that the user clicked on the button to open the SPL query in search from within the "Create Job" dialog. |
|
app.session.view_spl_clicked
|
Informs us that the user clicked on the button to open the SPL query in search from the main AD workflow UI. |
|
app.session.delete_job_successful
|
Deleting a job was successful. |
|
app.session.delete_missing_data_job_successful
|
Informs us that the user deleted a missing data job. |
|
app.session.aggregation_selected
|
Whether an aggregation method outside of the default (avg) was selected. |
|
app.session.time_span_selected
|
Whether a time span outside of the default for aggregation was selected. |
|
app.session.updated_job_saved
|
The user clicked save job after editing information. |
|
app.session.job_dashboard_open_in_search_clicked
|
The user clicked to open the SPL query associated with the job in search. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
The time policy score. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
The number of anomalies/ anomalous intervals detected in the data. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
The length of the seasonal/periodic component (if one is found) in the data. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
Whether the user is running the app with Splunk preinstalled dataset or with their own data. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
The top and bottom 5 anomaly confidence scores found in the data. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
How long our custom algorithm took to run. Encompasses all backend computation other than the SPL query execution time. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
Whether the user's data is evenly-spaced, and if so, what the resolution is. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
Whether or not the ensemble chose to use ADESCA algo. |
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app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
The number of missing/non-numeric values that were imputed. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
Number of anomalies detected that are non-contiguous. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
Example timestamps. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
Number of points in input time series. |
|
app.Splunk_App_for_Anomaly_Detection.anomalyapp
|
Value of sensitivity parameter provided by the user. |
|