🔍

Logical Or

Enrichments

Overview

The Logical OR enrichment performs boolean OR operations across multiple boolean attributes to create a new consolidated boolean attribute. This logical operator evaluates whether any of the selected boolean attributes contains a TRUE value, producing TRUE if at least one input is TRUE, and FALSE only when all inputs are FALSE. The enrichment is essential for combining multiple binary conditions, flags, or indicators into a single meaningful attribute that represents whether any of several conditions have been met.

In process mining and analysis, the Logical OR enrichment is particularly valuable when you need to identify cases that meet at least one of several criteria. For example, you might want to flag cases that have any type of exception, identify orders that triggered any kind of alert, or determine whether any compliance rule was violated. The enrichment operates at the case level, evaluating the boolean attributes for each case independently and storing the result as a new case attribute that can be used in further analysis, filtering, or visualization.

The enrichment intelligently handles null values by excluding them from the evaluation. If all selected attributes are null for a particular case, the result will also be null, preserving data integrity and avoiding false positives or negatives in your analysis.

Common Uses

  • Identify cases with any type of quality issue by combining multiple quality check flags
  • Flag orders that triggered any alert condition (payment alert OR delivery alert OR fraud alert)
  • Determine if any compliance violation occurred across multiple compliance checks
  • Detect processes that experienced any type of exception or error condition
  • Identify customers who qualify through any of multiple eligibility criteria
  • Mark cases requiring review if any review trigger condition is met
  • Consolidate multiple approval flags to determine if any approval was granted

Settings

New Attribute Name: Specify the name for the new boolean attribute that will store the OR operation result. Choose a descriptive name that clearly indicates what conditions are being combined. For example, use "Any_Exception_Occurred" when combining exception flags, or "Any_Approval_Granted" when combining approval statuses. The name must be unique and cannot conflict with existing attributes in your dataset.

Attribute Names: Select the boolean attributes you want to combine using the OR logic. You must select at least two boolean attributes for the operation. The enrichment will evaluate all selected attributes for each case and return TRUE if any of them is TRUE. Only boolean (True/False) attributes are available for selection. These can be original attributes from your dataset or boolean attributes created by other enrichments or calculators.

Examples

Example 1: Quality Control Alert System

Scenario: In a manufacturing process, multiple quality checks are performed at different stages. You need to identify products that failed any quality check to route them for detailed inspection.

Settings:

  • New Attribute Name: Any_Quality_Issue
  • Attribute Names: Visual_Inspection_Failed, Dimension_Check_Failed, Weight_Check_Failed, Functionality_Test_Failed

Output: Creates a new boolean attribute "Any_Quality_Issue" that is TRUE when any quality check failed:

Case ID Visual_Inspection_Failed Dimension_Check_Failed Weight_Check_Failed Functionality_Test_Failed Any_Quality_Issue
P-001 FALSE FALSE FALSE FALSE FALSE
P-002 TRUE FALSE FALSE FALSE TRUE
P-003 FALSE FALSE TRUE FALSE TRUE
P-004 TRUE TRUE FALSE TRUE TRUE

Insights: This consolidated flag enables quality managers to quickly identify all products requiring inspection, regardless of which specific test they failed, streamlining the quality control workflow.

Example 2: Customer Service Priority Routing

Scenario: A customer service center needs to identify high-priority support tickets that meet any of several escalation criteria for immediate attention.

Settings:

  • New Attribute Name: Requires_Immediate_Attention
  • Attribute Names: Is_VIP_Customer, Multiple_Contact_Attempts, Complaint_Contains_Legal_Terms, Service_Level_Breach

Output: The enrichment evaluates each case and sets "Requires_Immediate_Attention" to TRUE if any escalation criterion is met:

Case ID Is_VIP_Customer Multiple_Contact_Attempts Complaint_Contains_Legal_Terms Service_Level_Breach Requires_Immediate_Attention
CS-101 FALSE FALSE FALSE FALSE FALSE
CS-102 TRUE FALSE FALSE FALSE TRUE
CS-103 FALSE TRUE TRUE FALSE TRUE
CS-104 FALSE FALSE FALSE TRUE TRUE

Insights: Support managers can filter and prioritize tickets that require immediate attention, ensuring critical issues are addressed promptly regardless of the specific trigger.

Example 3: Fraud Detection in Financial Transactions

Scenario: A financial institution uses multiple fraud indicators to flag suspicious transactions. Any positive indicator should trigger a fraud review process.

Settings:

  • New Attribute Name: Potential_Fraud_Alert
  • Attribute Names: Unusual_Amount_Flag, Location_Mismatch, Velocity_Check_Failed, Blacklist_Match, Pattern_Anomaly_Detected

Output: Creates "Potential_Fraud_Alert" that triggers when any fraud indicator is positive:

Transaction Unusual_Amount_Flag Location_Mismatch Velocity_Check_Failed Blacklist_Match Pattern_Anomaly_Detected Potential_Fraud_Alert
TXN-8901 FALSE FALSE FALSE FALSE FALSE FALSE
TXN-8902 TRUE FALSE FALSE FALSE FALSE TRUE
TXN-8903 FALSE FALSE TRUE FALSE TRUE TRUE
TXN-8904 FALSE TRUE FALSE TRUE FALSE TRUE

Insights: The fraud team can immediately identify all transactions requiring review, enabling rapid response to potential fraud while the specific indicators provide context for investigation.

Example 4: Healthcare Patient Risk Assessment

Scenario: A hospital emergency department needs to identify patients who meet any criteria for high-risk classification to ensure appropriate care protocols.

Settings:

  • New Attribute Name: High_Risk_Patient
  • Attribute Names: Elderly_Patient, Chronic_Condition, Immunocompromised, Recent_Surgery, Critical_Vitals

Output: Evaluates multiple risk factors to identify high-risk patients:

Patient ID Elderly_Patient Chronic_Condition Immunocompromised Recent_Surgery Critical_Vitals High_Risk_Patient
PT-201 FALSE FALSE FALSE FALSE FALSE FALSE
PT-202 TRUE TRUE FALSE FALSE FALSE TRUE
PT-203 FALSE FALSE FALSE TRUE FALSE TRUE
PT-204 FALSE TRUE TRUE FALSE TRUE TRUE

Insights: Medical staff can quickly identify patients requiring enhanced monitoring or specialized care protocols, improving patient safety and care quality.

Example 5: Supply Chain Disruption Detection

Scenario: A logistics company monitors multiple indicators for potential supply chain disruptions and needs to flag shipments at risk from any disruption type.

Settings:

  • New Attribute Name: Disruption_Risk_Flag
  • Attribute Names: Weather_Alert, Port_Congestion, Carrier_Issue, Customs_Hold_Risk, Route_Restriction

Output: Combines multiple risk indicators to identify shipments potentially affected by disruptions:

Shipment Weather_Alert Port_Congestion Carrier_Issue Customs_Hold_Risk Route_Restriction Disruption_Risk_Flag
SH-5001 FALSE FALSE FALSE FALSE FALSE FALSE
SH-5002 TRUE FALSE FALSE FALSE FALSE TRUE
SH-5003 FALSE TRUE FALSE TRUE FALSE TRUE
SH-5004 FALSE FALSE FALSE FALSE NULL FALSE

Insights: Logistics coordinators can proactively manage shipments with any type of disruption risk, enabling contingency planning and customer communication.

Output

The Logical OR enrichment creates a new boolean case attribute with the name specified in the "New Attribute Name" setting. This attribute contains TRUE or FALSE values based on the OR logic evaluation of the selected input attributes.

Logical Operation: The enrichment implements the standard boolean OR operation:

  • Returns TRUE if at least one selected attribute is TRUE
  • Returns FALSE only if all selected attributes are FALSE
  • Returns NULL if all selected attributes are NULL

Null Value Handling: The enrichment intelligently handles null values in the input attributes:

  • Null values are excluded from the OR evaluation
  • If some attributes are NULL and others are FALSE, the result is FALSE
  • If some attributes are NULL and at least one is TRUE, the result is TRUE
  • Only when all attributes are NULL will the result be NULL

Data Type: The output attribute is always of boolean type, displaying as TRUE/FALSE, Yes/No, or 1/0 depending on your visualization settings in mindzieStudio.

Integration Capabilities: The new boolean attribute created by this enrichment can be:

  • Used as input for other logical enrichments (creating complex logical expressions)
  • Applied in case filters to select subsets of data
  • Utilized in conditional calculators and enrichments
  • Displayed in process maps to highlight cases meeting any criterion
  • Exported with your enriched dataset for external analysis
  • Combined with other boolean attributes in dashboards and reports

See Also

  • Negate - Inverts boolean values (NOT operation)
  • Boolean Count - Counts how many boolean attributes are TRUE
  • Case Compare Attributes - Compares attributes to create boolean results
  • Filter Cases - Use the OR result to filter your dataset

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

An error has occurred. This application may no longer respond until reloaded. Reload ??