BPM Integration Framework Using Intersection Coordination
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Solution Overview
Problem
Current integration tools and techniques for Business Intelligence (BI)/Business Performance Management (BPM) systems face challenges in efficiently exchanging data due to their generic nature, which is not suited for the fluid and varied environments of BPM systems, leading to high customization costs and complexity in data integration processes.
Innovation Solution
A high-performance, reusable BPM integration framework that employs an intersection coordination subsystem for keyless data synchronization and reload, optimized for SQL Server and Analysis Services technologies, using concatenated data intersection patterns instead of primary keys, and providing injectable integration templates for specific scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic ETL mechanisms are used for data integration, then broad compatibility with different data sources is achieved, but integration efficiency and performance deteriorate due to lack of optimization for specific BPM scenarios
Solution Approach 1:
The patent implements dynamic integration templates that can be selectively applied based on the specific data source and destination combination. Instead of using a single static ETL mechanism for all scenarios, the system dynamically selects and configures optimization rules appropriate for each BPM integration scenario, thereby maintaining both broad compatibility and scenario-specific efficiency
Solution Approach 2:
The patent changes the parameters of the ETL mechanism by introducing scenario-specific optimization rules and configuration options. Different integration templates with varying extraction, transformation, and load parameters are applied depending on the source system type (e.g., ERP, CRM, custom databases), allowing the system to adapt its behavior to maximize efficiency for each specific case while maintaining universal applicability
2Productivity
If custom ETL processes are developed for each scenario, then integration efficiency is improved, but device complexity and development cost increase
Solution Approach 1:
The patent creates a universal integration framework that incorporates multiple integration templates within a single system. Each template is designed to handle specific scenario types, but the overall framework provides a unified interface and management mechanism. This allows the system to achieve scenario-specific optimization without requiring separate custom developments for each case, thereby reducing overall complexity while maintaining high efficiency
Solution Approach 2:
The patent segments the integration process into standardized template modules that can be independently selected and configured. Rather than creating monolithic custom ETL processes for each scenario, the system divides the integration functionality into discrete, reusable template components (extraction templates, transformation templates, load templates) that can be组合 (combined) to address specific scenarios, reducing development complexity through modularity
3Reliability
If standardized data warehouse frameworks are used, then data consolidation is achieved, but adaptability to fluid BPM environments deteriorates
Solution Approach 1:
The patent introduces dynamic configuration capabilities to the data consolidation framework, allowing it to adapt to fluid BPM environments. The integration templates include configurable parameters and rules that can be adjusted based on the specific BPM scenario, source system characteristics, and data requirements, enabling the standardized framework to maintain both reliability and adaptability
Solution Approach 2:
The patent applies local quality by allowing different parts of the data consolidation process to have different properties and optimization rules tailored to specific BPM scenarios. Each integration template can be customized with scenario-appropriate extraction, transformation, and load configurations, enabling the overall framework to maintain standardized reliability while adapting locally to diverse BPM environment requirements
Data Source
AI summary
A method of data migration and integration with a data load mechanism. The first primary step is to extract the data from a named source by remote queries. The follow-up logic is used for any miscellaneous or supplemental transformations to inbound data for data renaming, null scrubbing, and data polishing. The filtration step allows for any extraneous data to be filtered out. The compression step consolidates any fragmented balances residual in the remote queries, localize remote data, data map, or follow-up logic. As a part of the compression process, the result set table signature is explicitly cast to that of the destination fact table. The result of this is an exact replica of the destination fact table format, data types and column order. The kickout handling provides referential integrity of the inbound data flow against the named BPM Destination System. Finally, the deployment step limits the dynamic reload or sync determination to certain fields.