Process Protocol Analysis via In-Memory APE Engine
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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, particularly when dealing with large datasets of process instances.
Innovation Solution
A method and system utilizing an Advanced Process Algebra Execution (APE) query language and engine that allows direct access to process protocols stored in-memory, enabling flexible and efficient analysis of complex processes by combining process operators with database functions, and visualizing results in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is extracted from source systems, transformed, and stored in a separate database for analysis, then data storage and retrieval is enabled, but analysis flexibility and performance deteriorate due to restricted predefined analyses and insufficient algorithm performance for large datasets
Solution Approach 1:
The system enables the database to serve multiple functions: both storing process data and performing complex analyses directly on the stored data using SQL:2016 standards. The database management system incorporates process mining capabilities, allowing it to function as both a storage repository and an analysis engine, thereby eliminating the need for separate analysis tools and improving analysis flexibility while maintaining data storage capacity
Solution Approach 2:
The invention changes the operational parameters of the database system by implementing advanced SQL:2016 standards with enhanced algorithms optimized for process data analysis. This includes using column-oriented storage, efficient indexing strategies, and parallel processing capabilities that enable the database to handle large datasets with high performance while providing flexible ad-hoc analysis capabilities
2Ease of operation
If predefined analyses are performed on process data, then analysis simplicity is maintained, but analysis precision and capability deteriorate for complex parallel processes
Solution Approach 1:
The system provides dynamic analysis capabilities where the analysis approach can adapt to the complexity of the process data. Simple predefined analyses remain available for basic needs, while the system simultaneously supports complex custom queries and advanced process mining algorithms for precise analysis of parallel processes. The database can dynamically select and combine different analysis methods based on the specific requirements of the data being analyzed
Solution Approach 2:
The database management system acts as an intermediary between simple query interfaces and complex analysis algorithms. It provides a layered architecture where users can access simple predefined analyses through easy interfaces, while the underlying system handles complex parallel process analysis using advanced algorithms, thereby maintaining both ease of operation and analysis precision
3Ease of manufacture
If traditional database algorithms are used for process analysis, then implementation simplicity is maintained, but processing performance deteriorates for large datasets with several hundred millions of process instances
Solution Approach 1:
The system changes the algorithmic parameters by implementing SQL:2016 standards with optimized algorithms specifically designed for process data. This includes using column-oriented storage formats, efficient compression techniques, and parallel query execution algorithms that dramatically improve processing performance for large datasets while maintaining implementation simplicity through standardized SQL interfaces
Solution Approach 2:
The invention replaces traditional row-oriented database algorithms with column-oriented algorithms optimized for analytical queries. This substitution enables efficient processing of large datasets by exploiting data locality, compression, and vectorized operations, thereby achieving high processing performance for hundreds of millions of process instances while maintaining ease of implementation through standard SQL
Data Source
AI summary
A computer-implemented method is provided for storing process data. The method comprises allocating a storage area for the process data in the storage means, loading the process data, from the at least one source system, and storing the process steps according to a predetermined data structure in the allocated storage area of the storage means. The predetermined data structure comprises a first attribute, in which a unique identification of the process instance of the respective process step is stored, a second attribute, in which an identification of the respective process step is stored, and a third attribute, in which the sequence of the process steps within a process instance is stored. The method then sorts the process steps in the allocated storage area, wherein the process steps are first sorted according to the first attribute, and subsequently, according to the third attribute.