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Statistics

The Audit Statistics page transforms audit log data into visual insights that help you analyze rule behavior, monitor trends, identify anomalies, and evaluate rule performance over time. Statistics can be generated for individual rules within selected date ranges. You can also compare two rules side by side to better understand differences in their execution patterns and results.

Selection Panel

Rule Selection

Use the Rule Picker to select the rule you want to analyze.

Each rule displays an audit log availability indicator:

  • 🟢 Green dot – Audit logs are available for the selected date range.

  • 🟠 Orange dot – No audit logs are available for the selected date range.

  • ( ⚪ Gray dot – Loading state. )

This allows you to quickly identify which rules contain analyzable data.

Date Range Selection

Statistics support the same date selection options as the Audit Logs page, including relative and absolute date ranges.

The date selection is shared between the Audit Logs and Statistics tabs. When you change the date range in one tab, the same range is automatically applied to the other tab.

All visualizations and calculations are generated from the selected period.

Data Dictionary (DD)

The Data Dictionary helps you explore the attributes available in audit logs and select the data you want to analyze.

Attributes are organized into three scopes:

  • Input - Data sent to the rule during execution.

  • Output - Data returned by the rule after execution.

  • Metadata - Technical and execution-related information, such as rule ID, rule name, version, status, timestamp, execution time, user ID, solver key, and correlation ID.

You can search for a specific attribute and select it to load detailed statistics into the main analysis area.

(DD) Attribute types

Each attribute in the Data Dictionary has an assigned type. The type is detected automatically from a sample of audit logs, so it should be understood as a best estimate rather than a fixed schema definition.

Supported attribute types are:

  • Number

  • String

  • Boolean

  • Object

  • Array

  • Mixed

  • Unknown

  • Date

Attribute Type Limitation

Because the type is inferred from sampled audit data, it may not always match every audit log in the selected range. For example, if an attribute appears as a number in most sampled records, it may be shown as Number. If different records contain different types for the same attribute, the attribute may be shown as Mixed. If the value is missing, empty, or cannot be reliably detected, it may be shown as Unknown.

(DD) Sampling and changed data models

The Data Dictionary is built from the latest matching audit logs in the selected date range and filters. To keep the page fast and responsive, the dictionary is based on up to 500 audit logs.

Sampling Limitation

The available attributes and their detected types reflect the data found in that 500 audit log sample. If your input or output model changed over time, the Data Dictionary may show attributes from the model version that appears in the selected audit logs. To inspect a specific model structure more accurately, narrow the date range or filter by a specific rule version.

(DD) Arrays and nested attributes

When an attribute contains an array, the Data Dictionary shows the array field itself and, when possible, also shows nested attributes found inside array items.

For example, if an input contains an array of customers, the dictionary may show the array field and also attributes found inside customer objects, such as customer ID, name, or status.

Array Inspection Limitation

For performance and readability, array inspection is limited. The system samples only a small number of object items from arrays and only scans nested data up to a safe depth. This prevents very large or deeply nested arrays from slowing down the statistics page.

(DD) Bulk array behavior

For input data, top-level arrays are handled as bulk-style inputs. When you select an attribute inside a bulk array, statistics can count values from individual array items instead of treating the whole array as one single value.

For example, if one audit log contains an input array with multiple submitted records, selecting a field inside those records can include values from each item in the array. This helps analyze batch or bulk rule executions more naturally.

For output data, statistics use the first output object when the stored output is wrapped in an array. This keeps output analysis consistent with the main rule result structure.

Main Visualizations

After selecting an attribute, multiple visualization types become available:

Frequency DistributionTime SeriesRaw Data Table

Rule Comparison (RC)

Statistics support side-by-side comparison of two rules.

To enable comparison:

  1. Enable the comparison switch.

  2. Select a secondary rule.

  3. Optionally choose a different date range for the secondary rule.

The system will display comparative visualizations whenever possible.

(RC) Attribute Compatibility

If the selected attribute does not exist in both rules, comparison cannot be performed for that attribute. In such cases, only the primary rule's statistics will be displayed.

Summary Statistics

Three summary metrics are automatically generated based on the selected attribute type.

Number Attributes

  • Minimum

  • Maximum

  • Average

Boolean Attributes

  • True Count

  • False Count

  • True Rate

Date Attributes

  • Earliest

  • Latest

  • Count

Object Attributes

  • Filled Values

  • Average Keys

  • Max Keys

Array Attributes

  • Filled Values

  • Average Items

  • Max Items

Mixed-Type Attributes

  • Dominant Type

  • Filled Values

  • Missing Values

Unknown Attributes

  • Filled Values

  • Missing Values

  • Total Records

String Attributes

  • Unique Values

  • Most Common

  • Count

Exporting Data

Statistics can be exported to CSV format for further analysis.

The export is downloaded as a ZIP file containing two files: one with the exported data and one with metadata describing the export.

Supported exports include:

  • Frequency Distribution data

  • Time Series data

  • Raw Data Table data

Integrations

Integrations modal provides ready-to-use REST API integration examples and Power BI setup information.

REST API snippets are available in cURL, JavaScript, Python, PHP, .NET, and Java, and are automatically generated using the currently selected BI API key, rule(s), and date range for retrieving Audit Logs.

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