ERP Database Event Segmentation for Accurate Transaction Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing ERP databases contain large quantities of event data that are not optimally structured for identifying useful information, leading to inefficiencies in business process analysis and potential errors in transaction flow identification.
Innovation Solution
A processor-readable medium executes instructions to filter, analyze, and transform ERP data into a standardized format, enabling identification of standard, non-standard, and incomplete transaction patterns, and perform remedial actions based on metric calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ERP data is stored in traditional database format, then data storage capacity is maintained, but data processing time increases and analysis accuracy decreases
Solution Approach 1:
The patent segments ERP data into distinct transaction events with specific event types, grouping them by process and variant. This segmentation transforms the monolithic database structure into organized transaction event sets, enabling faster processing and more accurate pattern identification by analyzing structured event sequences rather than raw database records.
Solution Approach 2:
The patent introduces an intermediary data transformation layer that converts traditional ERP database data into standardized transaction event formats. This intermediary structure acts as a mediator between the original database and analysis processes, improving both processing speed and identification accuracy without losing underlying data relationships.
2Reliability
If all ERP transaction data is analyzed without filtering, then comprehensive data coverage is achieved, but data processing complexity and time increase
Solution Approach 1:
The patent extracts and filters specific transaction events relevant to particular processes and variants from the broader ERP database. By taking out only the necessary transaction data for analysis while maintaining comprehensive coverage of relevant processes, the system reduces processing time without sacrificing analysis completeness.
Solution Approach 2:
The patent performs preliminary filtering and organization of ERP data into structured transaction events before analysis. This preliminary action of pre-processing and categorizing data by process and variant reduces the complexity of subsequent analysis while ensuring all relevant transactions are included, thereby maintaining reliability.
3Loss of information
If traditional ERP database structure is used, then data storage is maintained, but identification of useful information becomes difficult
Solution Approach 1:
The patent transforms the static ERP database structure into a dynamic transaction event model where data is organized by event types, processes, and variants. This dynamic structure adapts to different analysis needs by allowing flexible querying and grouping of transaction events, making useful information more accessible without increasing overall system complexity.
Data Source
AI summary
In an embodiment, a transaction dataset is analyzed to identify a set of transaction events, each transaction event from the set of transaction events associated with a transaction event type. The set of transaction events is analyzed to identify a plurality of sets of grouped transaction events, each set of grouped transaction events from the plurality of sets of grouped transaction events associated with a subset of transaction events from the set of transaction events. To identify a plurality of sets of variants, each set of grouped transaction events from the plurality of sets of grouped transaction events including an identifier of a variant for that set of grouped transaction events and from the plurality of sets of variants is analyzed, each set of variants from the plurality of sets of variants differing from each remaining set of variants from the plurality of sets of variants.