Process Mining Analytics Package for Event Log Structuring
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Solution Overview
Problem
Current process mining systems face challenges in analyzing business processes due to the lack of prepared event logs from IT systems, which are not designed to retrieve raw data traces of executed processes, and require non-technical users to navigate complex technical representations.
Innovation Solution
A method and system that provide analytics packages to a process mining system by structuring event logs with a predetermined data structure and auxiliary data, using process sensors to derive and generate process data, and creating graphical analyses and representations, allowing non-technical users to analyze business processes effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If process mining systems use raw data from IT systems directly, then data completeness is maintained, but data structure complexity increases and usability decreases
Solution Approach 1:
The patent segments raw process data into structured event logs with specific attributes (event ID, process step, timestamp, etc.). This segmentation transforms unstructured raw data into organized, queryable events while maintaining complete process information, resolving the contradiction between data completeness and structure complexity.
Solution Approach 2:
The patent introduces an intermediary layer (event log structure and data model) between raw IT system data and process mining analysis. This intermediary transforms raw data into a standardized format with predefined attributes, making the data usable for process mining without losing information, thus resolving the complexity issue.
2Productivity
If process mining systems provide technical event log representations, then analytical capability is maintained, but ease of operation for non-technical users decreases
Solution Approach 1:
The patent creates a copied and transformed representation of technical event logs in business-relevant terms. Instead of showing raw technical data, the system generates visualizations and analyses that copy the essential process information in a business-friendly format, maintaining analytical capability while improving accessibility for non-technical users.
Solution Approach 2:
The patent changes the parameters of data presentation from technical attributes to business-relevant metrics. Event logs are transformed with attributes like process step names, timestamps, and visual representations that align with business process terminology, making the system easier to operate for business users while preserving analytical depth.
3Reliability
If IT systems store raw process traces, then data fidelity is maintained, but retrieval readiness for event logs decreases
Solution Approach 1:
The patent applies preliminary action by pre-structuring raw process traces into event log format with standardized attributes during data collection. This preliminary transformation ensures that when process mining analysis is needed, event logs are already prepared and structured, eliminating retrieval delays while maintaining data fidelity from the original traces.
Data Source
AI summary
A computer-implemented method is provided for providing at least one analytics package to a process mining system, wherein the processor is provided with an event log comprising process data of business processes, the process data comprising at least one process element and the process element comprising at least one process step. The event log is stored according to a predetermined data structure comprising at least a first attribute for storing a unique identifier of the process element, a second attribute for storing an identifier of the process step, and a third attribute for storing an order of the process steps. The processor is further provided with auxiliary data and a data model, and the method comprises creating, based on the data model, at least one analytics package.