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Count Boolean Attributes With Value

Enrichments

Overview

The Count Boolean Attributes with Value enrichment is a specialized analytical tool that evaluates multiple boolean (true/false) attributes and counts how many of them match a specified value. This enrichment creates a new integer attribute containing the count of boolean attributes that are either TRUE or FALSE according to your selection, providing powerful capabilities for multi-criteria evaluation, compliance scoring, and risk assessment across your process cases.

This enrichment is particularly valuable when you need to evaluate cases against multiple binary conditions simultaneously. For example, in a compliance scenario where you have boolean flags for different regulatory requirements, this enrichment can count how many requirements are met (TRUE values) or violated (FALSE values). Similarly, in quality control processes with multiple pass/fail criteria, it can quantify the number of passed or failed checks. The enrichment supports both case-level and event-level boolean attributes, allowing for flexible analysis at different granularities of your process data.

Common Uses

  • Calculate compliance scores by counting how many regulatory requirements are met (TRUE) across multiple compliance flags
  • Assess risk levels by counting the number of risk indicators that are triggered (TRUE) in financial or operational processes
  • Measure quality by counting passed (TRUE) or failed (FALSE) quality check attributes in manufacturing processes
  • Evaluate customer satisfaction by counting positive (TRUE) responses across multiple satisfaction indicators
  • Track completion status by counting completed (TRUE) task flags in project management processes
  • Identify problematic cases by counting the number of exception flags (TRUE) or error indicators
  • Score vendor performance by counting met (TRUE) or missed (FALSE) SLA criteria across multiple metrics

Settings

Filter: An optional filter that allows you to limit the counting operation to specific cases or events. When a filter is applied, the boolean counting will only be performed for cases that meet the filter criteria. This is useful for calculating scores within specific time periods, for certain case types, or under particular conditions. If no filter is specified, the counting will be applied to all cases in your dataset.

New Attribute Name: The name for the new integer attribute that will store the count of boolean attributes matching your specified value. This attribute will be added to either your case table or event table depending on your source selection. Choose a descriptive name that clearly indicates what is being counted, such as "ComplianceScore", "QualityChecksPassed", "RiskIndicatorCount", or "RequirementsMet". This field is required.

Source: Determines whether to count boolean attributes from the case table or the event table. Select "Case" to count case-level boolean attributes (attributes that have one value per case), or "Event" to count event-level boolean attributes (attributes that can have different values for each event). The available boolean attributes for selection will update based on your source choice.

Attribute Names: A multi-select list that allows you to choose which boolean attributes to include in the counting operation. Only boolean (true/false) attributes from your selected source will be available for selection. You can select multiple attributes, and the enrichment will count how many of these selected attributes have the value you specify in the "Count If Value" setting. At least one attribute must be selected.

Count If Value: Specifies which boolean value to count - either TRUE or FALSE. If you select TRUE, the enrichment counts how many of the selected attributes have a TRUE value. If you select FALSE, it counts how many have a FALSE value. This allows you to measure either positive conditions (requirements met, checks passed) or negative conditions (violations, failures) depending on your analysis needs.

Examples

Example 1: Compliance Scoring in Financial Transactions

Scenario: A financial institution needs to calculate a compliance score for transactions by counting how many regulatory checks have been passed. They have multiple boolean attributes indicating whether specific compliance requirements are met.

Settings:

  • Source: Case
  • New Attribute Name: "Compliance Score"
  • Attribute Names: ["KYC_Verified", "AML_Check_Passed", "Sanctions_Clear", "Document_Complete", "Approval_Obtained", "Risk_Assessment_Done"]
  • Count If Value: True
  • Filter: None

Output: The enrichment creates a new case attribute "Compliance Score" with integer values representing the number of passed compliance checks:

  • Transaction TX-001: Compliance Score = 6 (all checks passed)
  • Transaction TX-002: Compliance Score = 4 (KYC and Risk Assessment not completed)
  • Transaction TX-003: Compliance Score = 5 (Sanctions check failed)
  • Transaction TX-004: Compliance Score = 3 (only basic checks completed)

Insights: Transactions with compliance scores below 5 are flagged for additional review. This quantitative score helps prioritize which transactions need immediate attention and identifies patterns in compliance gaps across different transaction types.

Example 2: Quality Control Assessment in Manufacturing

Scenario: A manufacturing plant evaluates products through multiple quality checkpoints, each recorded as a boolean attribute. They need to count failed checks to identify products requiring rework.

Settings:

  • Source: Case
  • New Attribute Name: "Failed Quality Checks"
  • Attribute Names: ["Visual_Inspection", "Dimension_Check", "Weight_Tolerance", "Electrical_Test", "Pressure_Test", "Final_Assembly"]
  • Count If Value: False
  • Filter: None

Output: The enrichment creates a "Failed Quality Checks" attribute showing the number of failed tests:

  • Product P-5001: Failed Quality Checks = 0 (all tests passed)
  • Product P-5002: Failed Quality Checks = 2 (Dimension and Weight failed)
  • Product P-5003: Failed Quality Checks = 1 (Electrical Test failed)
  • Product P-5004: Failed Quality Checks = 3 (Visual, Pressure, and Assembly failed)

Insights: Products with any failed checks require rework, while those with multiple failures may need complete remanufacturing. The count helps optimize rework routing and identify systemic quality issues in specific test categories.

Example 3: Risk Assessment in Loan Applications

Scenario: A bank evaluates loan applications using multiple risk indicators stored as boolean attributes. They need to count triggered risk flags to determine the overall risk level of each application.

Settings:

  • Source: Case
  • New Attribute Name: "Risk Indicators Count"
  • Attribute Names: ["High_Debt_Ratio", "Unstable_Employment", "Poor_Credit_History", "Insufficient_Collateral", "Previous_Default", "Income_Verification_Failed"]
  • Count If Value: True
  • Filter: Case_Type = "Personal Loan"

Output: The enrichment counts triggered risk indicators for personal loan applications:

  • Application LA-2024-101: Risk Indicators Count = 0 (low risk)
  • Application LA-2024-102: Risk Indicators Count = 2 (High_Debt_Ratio and Poor_Credit_History)
  • Application LA-2024-103: Risk Indicators Count = 4 (multiple risk factors)
  • Application LA-2024-104: Risk Indicators Count = 1 (only Unstable_Employment)

Insights: Applications with 0-1 risk indicators can be fast-tracked, 2-3 require additional review, and 4+ are automatically escalated to senior underwriters. This systematic scoring improves decision consistency and processing efficiency.

Example 4: SLA Performance Monitoring in IT Service Management

Scenario: An IT service desk tracks multiple SLA criteria as boolean attributes for each incident. They need to count met SLAs to calculate performance scores for different service categories.

Settings:

  • Source: Case
  • New Attribute Name: "SLA Criteria Met"
  • Attribute Names: ["Response_Time_Met", "Resolution_Time_Met", "First_Call_Resolution", "Customer_Satisfied", "Escalation_Avoided", "Documentation_Complete"]
  • Count If Value: True
  • Filter: Priority = "High"

Output: For high-priority incidents, the enrichment calculates SLA performance:

  • Incident INC-8001: SLA Criteria Met = 6 (perfect score)
  • Incident INC-8002: SLA Criteria Met = 4 (Resolution time and escalation issues)
  • Incident INC-8003: SLA Criteria Met = 5 (Documentation incomplete)
  • Incident INC-8004: SLA Criteria Met = 2 (multiple SLA breaches)

Insights: The quantified SLA performance enables data-driven improvements in service delivery. Incidents with low scores reveal systemic issues in specific SLA areas, guiding training and process optimization efforts.

Example 5: Multi-Criteria Vendor Evaluation

Scenario: A procurement team evaluates vendors across multiple performance criteria stored as boolean pass/fail attributes. They need to calculate an overall performance score for vendor ranking and selection.

Settings:

  • Source: Case
  • New Attribute Name: "Vendor Performance Score"
  • Attribute Names: ["On_Time_Delivery", "Quality_Standards_Met", "Price_Competitive", "Documentation_Accurate", "Responsive_Support", "Sustainability_Compliant"]
  • Count If Value: True
  • Filter: Evaluation_Period = "Q4-2024"

Output: The enrichment calculates vendor performance scores for Q4 evaluations:

  • Vendor V-101: Vendor Performance Score = 6 (excellent performance)
  • Vendor V-102: Vendor Performance Score = 4 (delivery and price issues)
  • Vendor V-103: Vendor Performance Score = 5 (documentation issues)
  • Vendor V-104: Vendor Performance Score = 3 (multiple performance gaps)

Insights: Vendors scoring 5-6 are preferred partners, 3-4 require improvement plans, and below 3 face potential contract termination. This objective scoring system supports strategic vendor management decisions and negotiations.

Output

The Count Boolean Attributes with Value enrichment creates a single new integer attribute in either the case table or event table, depending on your source selection. The attribute contains the count of selected boolean attributes that match your specified value (TRUE or FALSE).

For case-level counting, each case receives one count value representing the total number of matching boolean values across all selected attributes for that case. This count remains constant for all events within the case and is useful for case-level scoring, classification, and filtering.

For event-level counting, each event receives its own count value based on the boolean attribute values at that specific event. This allows for tracking how boolean conditions change throughout the process execution.

The output attribute can be used in subsequent analyses including:

  • Filtering cases based on score thresholds (e.g., show only cases with compliance score > 4)
  • Creating performance categories using the Categorize Attribute Values enrichment
  • Calculating average scores across case groups using aggregation calculators
  • Building predictive models using the score as a feature
  • Visualizing score distributions in dashboards and reports

The integer count provides a quantitative measure that transforms multiple binary evaluations into a single metric, enabling more sophisticated analysis and decision-making based on multi-criteria assessments.

See Also

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

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