Advanced Process Algebra Execution for Complex Business Process Analysis
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Solution Overview
Problem
Existing methods for analyzing business processes based on digital trails in IT systems are inflexible, inefficient, and inadequate for handling complex, potentially parallel processes, especially when dealing with large datasets.
Innovation Solution
A method and system utilizing an Advanced Process Algebra Execution (APE) query language to directly access and analyze process protocols stored in-memory, enabling flexible and efficient analysis of complex processes through advanced process operators and database functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is extracted from source systems, transformed, and stored in a separate database for analysis, then analysis can be performed on process data, but the analysis possibilities are restricted with respect to flexibility and performance
Solution Approach 1:
The patent extracts the analysis functionality directly from separate database systems and integrates it into the source IT systems themselves. This allows process data to be analyzed in-place without extraction and transformation steps, thereby maintaining analysis flexibility while significantly improving performance by eliminating data movement overhead.
Solution Approach 2:
The patent implements a universal analysis framework that can perform multiple types of process analyses (conformance checking, performance analysis, discovery, etc.) within a single system. This multi-functional approach eliminates the need for separate specialized analysis systems, improving both flexibility through diverse analysis capabilities and performance through unified data access.
2Productivity
If predefined analyses are performed on process data, then analysis can be carried out, but only limited analysis possibilities are available
Solution Approach 1:
The patent implements a dynamic analysis system where analysis configurations can be modified at runtime based on user needs. The system allows users to dynamically select different analysis types, adjust parameters, and customize query conditions without requiring predefined rigid analysis templates, thereby providing both high productivity through automated analysis and flexibility through user-configurable parameters.
Solution Approach 2:
The patent enables flexible analysis by allowing users to change analysis parameters dynamically. The system supports modifying analysis depth, time ranges, process instances, and other parameters without reconfiguring the entire analysis framework, enabling adaptable analysis while maintaining efficient processing through optimized parameter handling.
3Quantity of substance
If algorithms process very large datasets with several hundred millions of process instances, then comprehensive analysis is possible, but the performance of the algorithms is not sufficient
Solution Approach 1:
The patent segments large process datasets into manageable chunks or partitions that can be processed efficiently. By dividing the data processing task into smaller units that can be handled in parallel or sequentially without overwhelming system resources, the system achieves both comprehensive analysis of large volumes of process instances and acceptable processing performance.
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with optimized algorithms and data structures specifically designed for handling large volumes of process data. This includes using efficient data representations, optimized query execution plans, and memory management techniques that enable processing of hundreds of millions of process instances with improved performance.
4Adaptability or versatility
If analysis of very complex parallel processes is performed, then process insights can be obtained, but the analysis is imprecise
Solution Approach 1:
The patent introduces intermediary data structures and processing layers that facilitate the analysis of complex parallel processes. These intermediaries include process models, event logs with detailed timestamps, and intermediate calculation results that preserve precision information throughout the analysis chain, enabling accurate analysis of complex parallel workflows without loss of measurement precision.
Data Source
AI summary
A computer-implemented method is provided for analysis of process data. The method comprises receiving an APE statement (Advanced Process Algebra Execution), wherein the APE statement defines a query of process instances from the storage means, and wherein the APE statement comprises at least one process operator, and executing the APE statement and reading the process instances according to the APE statement from the storage means, and providing the result of the query for further processing.