Overview
The Filter List is a logical filter that combines multiple filters using AND logic, where a case must satisfy ALL filter conditions to be included in the results. This powerful case-level filter applies a sequence of filters one after another, with each filter operating on the results of the previous filter. Only cases that pass through all filters successfully remain in the final dataset, making it ideal for creating precise, multi-criteria filtering rules.
Common Uses
- Apply multiple criteria that must all be satisfied simultaneously
- Create complex filtering logic with multiple required conditions
- Build sequential filtering pipelines where each filter narrows the dataset
- Combine different filter types to create precise inclusion rules
- Implement business rules requiring multiple simultaneous qualifications
- Create rigorous compliance or quality filters with multiple mandatory criteria
Settings
Filter List: A collection of individual filters that will be applied sequentially using AND logic. Each filter in the list is applied to the results of the previous filter, so a case must pass all filters to remain in the final results.
How it works:
- The first filter is applied to the original dataset
- The second filter is applied to the results from the first filter
- This continues sequentially through all filters in the list
- Only cases that pass through all filters appear in the final results
Note: If no filters are in the list, the original dataset is returned unchanged. With just one filter, it behaves like that single filter.
Examples
Example 1: High-Value Regional Analysis
Scenario: You want to analyze high-value orders from the Eastern region only. Cases must be both from the Eastern region AND have order amounts exceeding $5,000. Both conditions are mandatory.
Settings:
- Filter 1: Cases with Attribute "Region" equals "Eastern"
- Filter 2: Cases with Attribute "Order Amount" greater than 5000
Result:
Only cases satisfying both conditions are included. Case #12345 from Eastern with $8,000 amount is included (passes both). Case #67890 from Eastern with $2,000 amount is excluded (fails amount test). Case #11111 from Western with $10,000 amount is excluded (fails region test). If you had 1,000 cases total with 300 Eastern cases, and 80 of those Eastern cases exceed $5,000, your result contains 80 cases.
Insights: This provides focused analysis on your target segment - high-value Eastern region orders. You can measure performance for this specific combination, identify best practices for handling high-value regional business, and optimize processing for this important customer segment.
Example 2: Completed High-Priority Cases
Scenario: You want to analyze only high-priority cases that have been completed, to measure how well your organization handled priority cases through to completion. Cases must be both Priority = "High" AND Status = "Completed."
Settings:
- Filter 1: Cases with Attribute "Priority" equals "High"
- Filter 2: Cases with Attribute "Status" equals "Completed"
Result:
Only completed high-priority cases are included. Case #ABC with Priority = "High" and Status = "Completed" is included (both conditions met). Case #DEF with Priority = "High" and Status = "In Progress" is excluded (not completed). Case #GHI with Priority = "Low" and Status = "Completed" is excluded (not high priority).
Insights: This shows you how high-priority cases performed when they reached completion. You can measure cycle time for completed priority cases, identify whether priority cases got faster processing, and analyze resource allocation effectiveness for your most important cases.
Example 3: Recent Manufacturing Quality Cases
Scenario: Your quality analysis should focus on recent manufacturing cases from the past 90 days that also had quality inspections performed. You need cases within the date range AND containing the "Quality Inspection" activity.
Settings:
- Filter 1: Time Period filter for last 90 days
- Filter 2: Cases containing Activity "Quality Inspection"
Result:
Only cases from the past 90 days that also include quality inspection events are retained. Recent cases without quality inspections are excluded. Older cases with quality inspections are also excluded. This gives you a focused dataset of recent, quality-checked production.
Insights: By combining recency with quality inspection requirement, you analyze current quality practices rather than historical or incomplete data. This shows whether quality inspection rates are improving and how recent quality-checked cases perform compared to historical data.
Example 4: Complex Compliance Filtering
Scenario: Compliance audit requires cases meeting multiple criteria - they must be from regulated industries, exceed the regulatory threshold, involve specific high-risk countries, and include manager approval. All four conditions are mandatory for audit inclusion.
Settings:
- Filter 1: Cases with Attribute "Industry" is one of ["Banking", "Healthcare", "Insurance"]
- Filter 2: Cases with Attribute "Transaction Amount" greater than 10000
- Filter 3: Cases with Attribute "Country" is one of ["Country X", "Country Y", "Country Z"]
- Filter 4: Cases containing Activity "Manager Approval"
Result:
Only cases passing all four filters are included in the audit sample. This might be a small subset (perhaps 2-3% of total cases), but these are precisely the cases requiring detailed regulatory review. Each filter eliminates cases not meeting that specific criterion, resulting in a highly focused compliance dataset.
Insights: This creates a precise audit population matching exact regulatory requirements. You ensure audit resources focus on genuinely relevant cases while documenting that your filtering logic matches regulatory criteria. Sequential AND logic guarantees no cases slip through that fail any single criterion.
Example 5: Resource-Specific Performance Analysis
Scenario: You want to analyze cases handled entirely by experienced resources in the Claims department that were also completed within SLA. Cases must meet all three conditions - correct department, resource experience level, and SLA compliance.
Settings:
- Filter 1: Cases with Attribute "Department" equals "Claims"
- Filter 2: Cases with Attribute "Resource Experience" equals "Senior"
- Filter 3: Cases with Attribute "SLA Compliance" equals "Met"
Result:
Only Claims department cases, handled by senior resources, that met SLA are included. This focused dataset shows optimal performance - experienced resources handling departmental work within target timeframes. Cases failing any single criterion are excluded.
Insights: This reveals performance when all optimal conditions are met - right department, experienced staff, successful outcome. You can use this as a benchmark for expected performance and compare it against cases where one or more conditions weren't met, helping identify which factors most impact performance.
Output
This filter operates at the case level using sequential AND logic:
- Applies filters one after another in sequence
- Each filter operates on the results of the previous filter
- Only cases passing all filters appear in final results
- Dataset size progressively decreases (or stays the same) through the filter sequence
- Preserves all case and event attributes for included cases
- Returns original dataset if no filters are in the list
- More restrictive than OR filter - fewer cases typically pass through
Use Filter List when you need cases to meet multiple simultaneous conditions, creating precise filtering rules that ensure all criteria are satisfied before including cases in your analysis.
This documentation is part of the mindzie Studio process mining platform.