Integrated Data Fabric for Dynamic Process Mining
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Solution Overview
Problem
Traditional process mining platforms require manual data extraction, lack dynamic analysis, struggle with incorporating external data, and lack simulation capabilities, leading to inaccurate insights and complex setup processes.
Innovation Solution
A platform that automates data extraction, integrates diverse data sources through an integrated data fabric, and supports simulation of process changes, enabling dynamic and goal-oriented process mining with low-code/no-code development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data extraction and preparation is used, then data can be extracted from various sources, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system automatically extracts and prepares data from multiple sources without requiring manual intervention. The process mining platform autonomously connects to data sources, retrieves relevant data, and prepares it for analysis, eliminating the time-consuming and error-prone manual preparation process while maintaining high accuracy.
Solution Approach 2:
The system performs data extraction and preparation actions in advance before the actual process mining analysis. By pre-extracting and pre-preparing data from various sources, the platform ensures that data is ready for immediate analysis, reducing overall processing time and eliminating manual intervention requirements.
2Measurement precision
If traditional data mining platforms are used, then data can be analyzed, but the analysis remains static and cannot account for dynamic process changes
Solution Approach 1:
The process mining platform provides dynamic analysis capabilities that adapt to changing processes in real-time. The system continuously monitors process execution, updates process models dynamically, and re-analyzes data to reflect current process state, enabling the platform to account for dynamic process changes rather than providing static historical analysis only.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor actual process execution against expected process models. This feedback mechanism allows the platform to detect deviations, update process models, and provide updated insights that reflect dynamic changes in process behavior, ensuring analysis remains accurate and current.
3Quantity of substance
If traditional platforms are used, then data from initial datasets can be analyzed, but additional data sources cannot be easily incorporated
Solution Approach 1:
The process mining platform is designed with universal data connection capabilities that allow it to integrate with multiple diverse data sources without requiring separate complex integration processes. The platform can connect to various internal and external data sources, including databases, cloud services, and other systems, through standardized interfaces that simplify data incorporation while maintaining the ability to handle diverse data formats and structures.
4Reliability
If complex setup and configuration processes are used, then data mining operations can be performed, but accessibility and speed are limited
Solution Approach 1:
The platform performs self-configuration and automated setup processes, eliminating the need for complex manual configuration. The system automatically detects data sources, establishes connections, configures analysis parameters, and sets up mining operations without requiring users to manually configure complex settings, thereby improving accessibility and speed while maintaining operational reliability through automated validation and error checking.
Data Source
AI summary
The disclosed system and methods relate to guided process mining. A system includes a processor and memory configured to provide a graphical user interface to a user device. The interface includes a user-selectable-parameter element and representations of processes. Upon user selection of a process, a guided investigation is launched based on the current setting of the user-selectable-parameter element. Upon completion of the investigation, a second graphical user interface is provided, configured to present data regarding the process based on user interactions during the investigation. The system also includes methods for process mining using integrated data from multiple systems, and for generating templated objects for process mining.