Path Finding Method for Model Structures
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Solution Overview
Problem
Current approaches for evaluating complex modeled structures in Business Process Management (BPM) require expert knowledge and are limited by the user's understanding of methodologies, making it difficult for non-technical users to define relevant data sets for analysis and reporting, often resulting in incomplete or inconsistent results due to the need for complex scripting and programming.
Innovation Solution
A path finding method that allows users to specify a source and target object within a model, generating a list of all possible paths and enabling users to exclude irrelevant paths using user-specified limitations such as maximum path length and stopovers, without requiring detailed knowledge of the underlying model or system architecture, facilitating a 'subtractive' approach to data selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an additive approach is used to define relevant data sets step-by-step, then the completeness of data selection improves, but the complexity of the process increases and requires expert knowledge
Solution Approach 1:
The patent inverts the traditional additive approach by implementing a subtractive method. Instead of building up data sets step-by-step through complex scripting, the system automatically generates complete path sets and allows users to exclude unwanted paths through simple filtering. This reverses the problem-solving direction from construction to elimination, reducing the need for expert knowledge while maintaining completeness.
Solution Approach 2:
The system performs automatic path generation and data set compilation without requiring user intervention in the complex scripting phase. The automated path finding engine independently identifies all possible paths between source and target objects, freeing users from needing to understand complex methodologies or write scripts, thereby reducing process complexity while ensuring comprehensive data coverage.
2Measurement precision
If complex scripting languages and SQL statements are used for query definition, then the precision of data retrieval improves, but the ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediary layer between the user and the complex data retrieval system. The automatic path finding engine acts as a mediator that translates simple user-defined source and target objects into comprehensive path sets, eliminating the need for users to directly write complex SQL statements or scripting code while maintaining precise data retrieval capabilities.
Solution Approach 2:
The system creates a simplified representation of the data retrieval process where users work with conceptual source and target objects rather than complex queries. This copying approach allows users to define their needs in simple terms while the system handles the complex translation and execution, improving ease of operation without sacrificing retrieval precision.
3Loss of information
If the complete set of paths is automatically generated, then the completeness of information improves, but the quantity of data to be processed increases
Solution Approach 1:
The patent extracts only the relevant information needed for user decision-making by implementing exclusion mechanisms. After automatically generating the complete set of paths to ensure information completeness, the system allows users to exclude unwanted paths through simple filtering criteria, removing excess data while preserving the comprehensive information-gathering benefit of the initial complete generation.
Solution Approach 2:
The system initially performs excessive action by generating all possible paths beyond what any single user might immediately need, ensuring no relevant information is missed. This complete generation is then followed by selective exclusion, allowing users to trim the data set to manageable sizes while maintaining confidence that all potentially relevant paths were initially considered.
4Manufacturing precision
If expert knowledge is required to define output formats and evaluate data, then the manufacturing precision of analysis results improves, but the productivity of the process deteriorates
Solution Approach 1:
The system performs self-service by automatically generating comprehensive path sets and compiling complete data sets without requiring expert intervention. The automated path finding engine independently handles the complex methodology and data compilation, freeing experts from time-consuming manual work while maintaining high precision through the system's built-in understanding of the underlying methodologies.
Solution Approach 2:
The automatic path finding engine serves as an intermediary that bridges the gap between simple user requirements and complex analysis needs. It translates user-defined source and target objects into comprehensive path sets using embedded knowledge of methodologies, eliminating the need for experts to manually define output formats and evaluate data, thereby improving productivity while maintaining precision.
Data Source
AI summary
Certain example embodiments described herein relate to defining relevant data for analysis and reporting for modeled processes and process-related data. In certain example embodiments, a normal (and potentially-non-expert) user can define a model, object, or a relation as “source” and“target” of interest. The user also may be able to make a question more concrete, e.g., by specifying stopovers, a number of steps that might be between the source and the target, etc. A net of different paths from the source to the target may be retrieved as result, representing all possible relations, including those that are implicit. Such information also can be selectively excluded from the net, e.g., if the user does not want to evaluate same. However, the user can be reasonably sure that all relevant data for analysis has been returned, even though the definition was made without consulting an expert.