Data Transformation Execution Schedule Optimization
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Solution Overview
Problem
Enterprise data scattered across different systems and applications leads to latency, inconsistency, and increased costs due to the overhead of integrating and transforming data, making it challenging to optimize the transformation process efficiently.
Innovation Solution
A framework that communicates with various business management systems to unify and transform data using optimization algorithms, such as the decrease time algorithm and critical path algorithm, to generate an execution schedule that minimizes processor idle times and overall transformation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If enterprise data is unified or converted through traditional infrastructure, then data consistency and user experience are improved, but transformation cost and time increase due to professional expertise requirements and operational overheads
Solution Approach 1:
The system enables self-service data transformation by automatically discovering data sources, generating transformation code, and executing conversions without requiring professional expertise. The autonomous agents perform tasks that traditionally needed human professionals, reducing both time and cost while maintaining data consistency through systematic approaches.
Solution Approach 2:
Manual mechanical processes of data transformation are replaced with automated software agents and algorithms. The system substitutes human professionals with intelligent systems that can automatically analyze data sources, generate transformation logic, and execute conversions, thereby reducing transformation time and operational overheads.
2Reliability
If enterprise data is unified or converted through traditional infrastructure, then data consistency and user experience are improved, but transformation cost and time increase due to professional expertise requirements and operational overheads
Solution Approach 1:
Autonomous agents serve as intermediaries between fragmented data sources and the unification process. These agents automatically discover, analyze, and transform data from multiple sources without requiring complex manual integration infrastructure, thereby reducing infrastructure complexity while maintaining data consistency.
Solution Approach 2:
Complex manual integration infrastructure is replaced with automated software-based agents. The system substitutes physical and manual processes with intelligent software systems that can handle data unification through automated discovery, analysis, and transformation, reducing overall infrastructure complexity.
3Productivity
If optimization algorithms are used to generate execution schedules for data transformation, then transformation time is reduced, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by automatically discovering data sources and analyzing transformation requirements before generating execution schedules. This preliminary analysis enables the optimization algorithms to work with well-defined parameters, reducing the complexity burden while achieving high transformation speeds through pre-planned execution paths.
Solution Approach 2:
The execution schedule generation is made dynamic through autonomous agents that can adapt to different data sources and transformation requirements. The system dynamically generates optimized schedules based on real-time analysis of data characteristics, maintaining high productivity while managing complexity through adaptive rather than static approaches.
Data Source
AI summary
Methods and system are disclosed that generate an execution schedule to optimize a transformation of business. In one aspect, from multiple tables residing in multiple databases and storing business data associate with multiple business management systems, dependencies between the tables may be determined based on attributes associated with the tables. When execution time for transforming business data exists, a decrease time algorithm or a critical path algorithm may be executed to generate execution schedule and to calculate processor idle times during the transformation of business data. Based on the calculated processor idle times, whether or not to execute a local optimization algorithm may be determined. Based on the determination, execution schedule that optimize the transformation of business data may be generated. The transformation of business data may be executed based to the generated execution schedule that optimizes a time consumed for transforming the business data in the tables.