Integrated Data Fabric for Guided Process Mining and Simulation
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Solution Overview
Problem
Traditional process mining platforms require manual data extraction, lack dynamic analysis, cannot incorporate external data sources, and lack simulation capabilities, leading to inaccurate insights and complex setup processes that hinder efficient process improvement.
Innovation Solution
A platform with integrated data fabric and closed-loop mining capabilities that automates data extraction, prioritizes insights, and supports simulation, enabling efficient, dynamic, and goal-oriented process mining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual data extraction and preparation is performed, then data can be extracted from various sources, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service data extraction by allowing users to select data sources and parameters through graphical interfaces without manual intervention. The platform automatically extracts, transforms, and loads data from multiple sources including databases, cloud storage, and APIs, eliminating the need for manual data preparation while reducing time consumption and errors.
Solution Approach 2:
The system performs preliminary data extraction and preparation actions automatically before analysis. Users can pre-configure data sources, transformation rules, and loading parameters in advance, and the system executes these preparations autonomously, saving significant time and reducing manual effort in the data preparation phase.
2Adaptability or versatility
If traditional data mining platforms are used, then data can be analyzed, but insights are limited to factors captured in the initial dataset and require lengthy data gathering
Solution Approach 1:
The system provides universal data integration capabilities by supporting multiple data sources including relational databases, NoSQL databases, cloud storage, APIs, and file systems. Users can connect to diverse data sources through standardized interfaces, and the platform automatically adapts to different data formats and structures, enabling comprehensive analysis beyond initial datasets without requiring lengthy data gathering processes.
Solution Approach 2:
The system introduces an intermediary data fabric layer that mediates between diverse data sources and analysis operations. This intermediary layer handles data transformation, integration, and access uniformly, allowing the system to incorporate additional data sources and factors dynamically without requiring users to perform lengthy data gathering or configuration tasks for each new source.
3Ease of operation
If complex setup and configuration processes are required, then data mining operations can be performed, but accessibility and speed are limited
Solution Approach 1:
The system segments the complex data mining process into discrete, user-friendly components through graphical interfaces. Users can independently configure data sources, selection criteria, analysis parameters, and visualization options without needing to understand the overall system complexity. Each component can be configured separately and automatically integrated, making the platform accessible to users without data science or coding expertise while maintaining full analytical capability.
4Measurement precision
If traditional platforms provide lists of potential issues, then insights can be identified, but prioritization based on impact or relevance is lacking
Solution Approach 1:
The system incorporates feedback mechanisms that continuously refine insight prioritization based on user interactions, process goals, and business context. Users can provide feedback on the relevance and impact of identified insights, and the system uses this feedback to adjust prioritization algorithms. This feedback loop ensures that insights are ranked according to their actual relevance to organizational goals, filtering out less relevant information and focusing on high-impact areas.
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.