Enterprise Process Graph for RPA Data Integration
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Solution Overview
Problem
Conventional methods for analyzing data from robotic process automation (RPA) discovery techniques lack a unified representation of enterprise processes, making it difficult to gain a comprehensive understanding of RPA implementations.
Innovation Solution
Generating an enterprise process graph by combining data from multiple discovery techniques such as process mining, task mining, and task capture, which involves creating event tables, normalizing data, and connecting graphs to create a unified representation of RPA processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual analysis of discovery techniques is performed, then each technique can be evaluated separately, but a unified understanding of enterprise processes is not achieved
Solution Approach 1:
The patent combines multiple discovery techniques (process mining, task mining, task capture) into a unified enterprise process graph that integrates data from all sources. This merging allows simultaneous evaluation of individual techniques while achieving comprehensive unified understanding of enterprise processes, resolving the contradiction between separate evaluation and integrated insight.
2Loss of information
If multiple discovery techniques are integrated into a unified representation, then comprehensive understanding is achieved, but system complexity increases
Solution Approach 1:
The patent segments the integration process into distinct modules: data collection from multiple discovery techniques, event table generation for each technique, normalization processing, and graph connection. This segmentation manages system complexity by organizing the integration into manageable, independent components that can be developed and maintained separately while achieving unified process understanding.
Solution Approach 2:
The patent introduces event tables as intermediary structures that mediate between raw data from multiple discovery techniques and the final unified enterprise process graph. These event tables serve as standardized intermediate representations that facilitate integration while isolating complexity, allowing different data sources to be normalized and connected systematically.
3Quantity of substance
If data from multiple discovery techniques is collected and integrated, then comprehensive process data is obtained, but data processing time increases
Solution Approach 1:
The patent performs preliminary normalization of data from each discovery technique into standardized event tables before integration. This preliminary processing organizes data in advance, creating consistent structures that accelerate the subsequent graph generation and integration processes, reducing overall processing time despite handling large volumes of data from multiple sources.
Data Source
AI summary
Systems and methods for generating an enterprise process graph are provided. Sets of process data relating to an implementation of RPA (robotic process automation) acquired using a plurality of discovery techniques is received. An enterprise process graph representing the implementation of RPA is generated based on the received sets of process data.