Parallel Price Master Database Lookup System
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Solution Overview
Problem
Current price calculation systems experience degraded response times due to resource-intensive database lookups in price master databases, which necessitate caching that increases memory usage and total cost of ownership, and complex cache management that affects system stability.
Innovation Solution
The system generates sub-procedures for price calculation rules that can be executed in parallel within an in-memory database, eliminating the need for caching and optimizing database lookups by grouping similar table accesses for faster execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If price master data is cached in shared memory to avoid response time degradation, then response time is improved, but memory usage increases and total cost of ownership increases
Solution Approach 1:
The patent segments the monolithic database lookup process into multiple independent sub-procedures that can be executed in parallel. By dividing the pricing calculation into separate lookup operations for different price master data tables (e.g., condition tables, access tables, calculation tables), the system achieves faster overall processing without requiring large amounts of cache memory, thus resolving the contradiction between response time and memory usage.
Solution Approach 2:
The patent performs preliminary actions by pre-generating and storing SQL scripts for price master data lookups in a repository. These pre-prepared SQL statements are executed during pricing calculations, eliminating the need for complex real-time database queries and reducing response time without requiring extensive caching, thereby addressing the contradiction between response time and memory consumption.
2Reliability
If cache handlers employ additional services to manage cached data, then data freshness is maintained, but system resources are occupied and system stability deteriorates
Solution Approach 1:
The patent extracts the complex cache management functionality from the system and replaces it with a simplified approach using pre-generated SQL scripts stored in a repository. Instead of employing additional cache handler services to manage data freshness, the system uses straightforward script execution with optional validation against the price master database, significantly reducing system complexity while maintaining data reliability.
Solution Approach 2:
The patent uses disposable, short-lived SQL script objects stored in a repository instead of maintaining complex, long-lived cache structures. Each pricing calculation uses fresh or validated SQL scripts that can be quickly executed and discarded, eliminating the need for complex cache management services and reducing system complexity while ensuring data freshness through optional validation.
3Measurement precision
If multiple database lookups are performed sequentially for price calculations, then accuracy is maintained, but response time degrades
Solution Approach 1:
The patent segments the sequential database lookup process into multiple independent sub-procedures that can execute in parallel. By dividing the pricing calculation into separate lookup operations for different price master data tables and executing them concurrently using thread pools or asynchronous processing, the system maintains pricing accuracy while dramatically reducing response time.
Solution Approach 2:
The patent transitions from sequential execution (one-dimensional time progression) to parallel execution by introducing a new dimension of concurrent processing. Multiple database lookups that were previously performed one after another are now executed simultaneously using multi-threading or asynchronous operations, maintaining accuracy through proper result aggregation while reducing overall response time.
Data Source
AI summary
A system generates a plurality of price master data lookup procedures that are capable of being executed in parallel. The plurality of price master data lookup procedures is stored in a repository in an in-memory system. The in-memory system includes a parallel processor. The system accesses the repository of price master data lookup procedures, and retrieves price master data in parallel using the price master data lookup procedures. The system then calculates a price using the price master data retrieved from the price master database in the in-system memory.