Process Mining Repository Template for Self-Service Data Analysis

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Solution Overview

Problem

Traditional process mining tools require specialized technical skills and are labor-intensive, leading to slow adoption rates and reduced value in data-driven decision making due to the complexity of analyzing large-scale process data.

Innovation Solution

A computer-implemented method generates a process mining repository by providing a template with predefined rules to validate and select relevant data models, reducing manual effort and enabling non-technical users to analyze process data through a self-serviceable environment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional process mining tools are used with custom source systems, then process data can be extracted and analyzed, but the complexity and cognitive challenge increase significantly requiring specialized technical skills

Engineering Contradiction:
Improveanalysis result validityVSAvoidsystem complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces a process mining repository template as an intermediary layer between the process mining tool and custom source systems. This template contains predefined queries, dashboards, and data models that mediate the complex interaction between users and the source systems, shielding users from technical complexity while ensuring reliable analysis results through validated data models

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent performs preliminary actions by pre-configuring the process mining repository template with validated data models, predefined queries, and dashboard configurations before actual process mining analysis. This preliminary setup includes validating data model compatibility with source systems in advance, so that when users conduct analysis, the complex validation and configuration work has already been completed

Inventive Principle:
Principle #10Preliminary action

2Productivity

If specialized analysts are trained to use process mining tools, then data-driven decision making can be performed, but the time and training effort required increase significantly

Engineering Contradiction:
Improvedata-driven decision making capabilityVSAvoidtraining time
Core Design Contradiction:
ProductivityVSLoss of time

Solution Approach 1:

The patent enables self-service by providing a process mining repository template that allows business users to independently configure and execute process mining analyses without requiring specialized training. The template contains pre-built data models and queries that users can select and customize based on their specific needs, eliminating the need for extensive technical training while maintaining productive data-driven decision-making capabilities

Inventive Principle:
Principle #25Self-service

3Loss of information

If manual dashboard construction is performed to monitor process performance, then current process performances can be captured, but the labor effort and time required increase significantly

Engineering Contradiction:
Improveprocess performance informationVSAvoidanalysis speed
Core Design Contradiction:
Loss of informationVSProductivity

Solution Approach 1:

The patent performs preliminary action by pre-configuring dashboard templates with relevant process performance metrics, queries, and visualizations before actual monitoring needs arise. These pre-built dashboards contain validated queries that automatically capture process performance information, eliminating the need for manual dashboard construction while ensuring comprehensive performance monitoring

Inventive Principle:
Principle #10Preliminary action

Data Source

PatentEP4361840A1Process mining repository for analyzing process data
Publication Date: 2024.05.01 CELONIS SE
  • EP4361840A1 patent drawingFigure 1
  • EP4361840A1 patent drawingFigure 2
  • EP4361840A1 patent drawingFigure 3

AI summary

The present invention relates to a computer-implemented method to generate a process mining repository for analyzing process data, wherein the process data is a multidimensional large-scale dataset which is extracted from at least one external computer system and transformed into a number of data models. The process mining repository represents a process workspace, in which the user may conduct process mining on a valid data model, i.e., explore process data, for instance, using a dynamic question and answer framework.