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Negate

Enrichments

Overview

The Negate enrichment performs logical negation on boolean attribute values, inverting TRUE to FALSE and FALSE to TRUE, and storing the result in a new attribute. This fundamental logical operator provides essential capabilities for identifying inverse conditions, finding exceptions to rules, and creating complementary logic in your process analysis. By flipping boolean values, the Negate enrichment enables you to easily identify cases that don't meet certain criteria, highlight process deviations, and build more sophisticated conditional logic in your analyses.

The Negate enrichment is particularly valuable in process mining scenarios where you need to understand the opposite of existing conditions. For instance, you can identify cases that are NOT compliant when you have a compliance flag, find activities that did NOT occur on time when you have an on-time indicator, or highlight exceptions to standard processing rules. This enrichment works seamlessly with other logical operators like OR and comparison operators, allowing you to build complex boolean expressions that capture nuanced business rules and conditions in your process data.

Common Uses

  • Identify non-compliant cases by negating a compliance flag attribute
  • Find delayed processes by inverting an "on-time" boolean indicator
  • Highlight exceptions by negating standard processing condition attributes
  • Create inverse filters for analyzing what did NOT happen in a process
  • Build complex logical conditions by combining negation with other boolean operators
  • Identify missing approvals by negating approval status flags
  • Find incomplete cases by inverting completion status attributes

Settings

New Attribute Name: Specify the name for the new attribute that will store the negated boolean value. Choose a descriptive name that clearly indicates the inverted logic. For example, use "Non_Compliant" when negating a "Compliant" attribute, or "Delayed" when negating an "On_Time" attribute. The name must be unique and cannot conflict with existing attributes in your dataset.

Attribute Names: Select the boolean attribute whose values you want to negate. Only boolean attributes (TRUE/FALSE) are available for selection. The attribute must already exist in your dataset - you can use boolean attributes from the original data or those created by other enrichments such as comparison operators or conformance checks. The selected attribute's values will be inverted to create the new attribute.

Examples

Example 1: Identifying Non-Compliant Purchase Orders

Scenario: In a procurement process, you have a boolean attribute "Meets_Budget_Guidelines" that indicates whether each purchase order stays within budget limits. You need to identify and analyze orders that exceed budget guidelines for special review.

Settings:

  • New Attribute Name: Exceeds_Budget
  • Attribute Names: Meets_Budget_Guidelines

Output: Creates a new case attribute "Exceeds_Budget" with inverted values:

  • Cases where Meets_Budget_Guidelines = TRUE → Exceeds_Budget = FALSE
  • Cases where Meets_Budget_Guidelines = FALSE → Exceeds_Budget = TRUE
  • Cases where Meets_Budget_Guidelines = null → Exceeds_Budget = null

Insights: This inverted attribute makes it easy to filter and analyze purchase orders that require budget exception approval, helping procurement teams focus on high-risk transactions and understand patterns in budget overruns.

Example 2: Finding Delayed Patient Treatments

Scenario: A hospital tracks whether emergency room patients are seen within the target time using a "Seen_Within_Target" boolean attribute. Healthcare administrators need to identify delayed cases for process improvement initiatives.

Settings:

  • New Attribute Name: Treatment_Delayed
  • Attribute Names: Seen_Within_Target

Output: For each patient case, creates "Treatment_Delayed":

  • Patient seen within 2 hours (Seen_Within_Target = TRUE) → Treatment_Delayed = FALSE
  • Patient wait exceeded 2 hours (Seen_Within_Target = FALSE) → Treatment_Delayed = TRUE

This allows easy identification of all delayed cases for root cause analysis.

Insights: The negated attribute enables quick filtering of delayed treatments, helping identify patterns in delays by time of day, department, or patient acuity level, leading to targeted process improvements.

Example 3: Detecting Missing Approvals in Loan Processing

Scenario: A financial institution has a boolean attribute "Manager_Approval_Received" for loan applications. Compliance officers need to identify applications processed without proper managerial approval.

Settings:

  • New Attribute Name: Missing_Manager_Approval
  • Attribute Names: Manager_Approval_Received

Output: Creates "Missing_Manager_Approval" for each loan application:

  • Applications with approval (Manager_Approval_Received = TRUE) → Missing_Manager_Approval = FALSE
  • Applications without approval (Manager_Approval_Received = FALSE) → Missing_Manager_Approval = TRUE

Insights: This inverted flag immediately highlights compliance violations, enabling quick remediation and helping prevent regulatory issues. It can be used in dashboards to monitor approval compliance rates in real-time.

Example 4: Identifying Incomplete Manufacturing Orders

Scenario: A manufacturing company tracks order completion with a "Quality_Check_Passed" boolean attribute. Production managers need to identify orders that failed quality checks for rework planning.

Settings:

  • New Attribute Name: Requires_Rework
  • Attribute Names: Quality_Check_Passed

Output: For each manufacturing order:

  • Orders passing quality (Quality_Check_Passed = TRUE) → Requires_Rework = FALSE
  • Orders failing quality (Quality_Check_Passed = FALSE) → Requires_Rework = TRUE

Sample data showing the transformation:

  • Order #1001: Quality_Check_Passed = TRUE → Requires_Rework = FALSE
  • Order #1002: Quality_Check_Passed = FALSE → Requires_Rework = TRUE
  • Order #1003: Quality_Check_Passed = TRUE → Requires_Rework = FALSE

Insights: The negated attribute helps production teams quickly identify and prioritize orders requiring rework, estimate rework capacity needs, and analyze root causes of quality failures.

Example 5: Finding Unresolved Customer Service Tickets

Scenario: A customer service department has a "Ticket_Resolved" boolean attribute. Service managers need to focus on unresolved tickets to improve response times and customer satisfaction.

Settings:

  • New Attribute Name: Still_Open
  • Attribute Names: Ticket_Resolved

Output: Creates "Still_Open" attribute for service tickets:

  • Resolved tickets (Ticket_Resolved = TRUE) → Still_Open = FALSE
  • Unresolved tickets (Ticket_Resolved = FALSE) → Still_Open = TRUE

This enables immediate filtering of all open tickets requiring attention.

Insights: The inverted attribute facilitates real-time monitoring of open ticket volumes, helps identify aging unresolved issues, and enables trend analysis of resolution rates over time.

Output

The Negate enrichment creates a new boolean case attribute with the name specified in the "New Attribute Name" setting. The output attribute contains the logical inverse of the input boolean values.

Truth Table:

  • Input: TRUE → Output: FALSE
  • Input: FALSE → Output: TRUE
  • Input: null → Output: null (remains null, not negated)

Data Type: The output attribute is always of type Boolean, displayed according to your mindzieStudio display format settings (typically "Yes/No" or "True/False").

Null Value Handling: If the source attribute contains a null value for a particular case, the negated attribute will also be null for that case. The enrichment does not convert null values to FALSE or TRUE - it preserves the null state to maintain data integrity and avoid incorrect assumptions about missing data.

Integration with Other Features: The negated attribute can be used immediately in:

  • Filters to focus on specific subsets of cases (e.g., filter on Still_Open = TRUE)
  • Other logical enrichments like "Logical OR" to build complex conditions
  • Calculators to count or analyze negated conditions
  • Conformance checking to identify process violations
  • Dashboards and reports for monitoring inverse KPIs

The attribute appears in all attribute selection lists throughout mindzieStudio and maintains full compatibility with export functions and external analysis tools.

See Also

  • Logical OR - Combine multiple boolean attributes with OR logic
  • Compare Two Attributes - Create boolean attributes by comparing values
  • Count Boolean Attributes with Value - Count how many boolean attributes have specific values
  • Combine Boolean Attributes - Concatenate names of TRUE boolean attributes

This documentation is part of the mindzie Studio process mining platform.

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