Referential Data Structures for Real-Time Asset Valuation
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Solution Overview
Problem
Existing systems face challenges in maintaining real-time updates of asset attributes and valuations in a network environment, particularly in handling fungible assets across multiple markets, and lack efficient mechanisms for matching buy and sell orders while ensuring relative value equivalence and compliance with Sharia law.
Innovation Solution
A server-based system that utilizes a database with referentially linked data structures to automatically update asset attributes in real-time, allowing for cycling, liquidating, and replenishing of assets based on streaming data, while maintaining relative value equivalence and aligning orders with Sharia law through a weighted average benchmark and cryptographic authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time updates of asset attributes are implemented using traditional database structures, then asset valuation accuracy is improved, but system complexity and processing time increase significantly
Solution Approach 1:
The database is segmented into two distinct data structures: a first data structure storing asset attributes and a second data structure storing trading data. This segmentation allows independent management and updating of each structure, reducing overall system complexity while maintaining real-time valuation accuracy through referential links between the structures.
Solution Approach 2:
A referential link acts as an intermediary between the first data structure (asset attributes) and the second data structure (trading data). This intermediary mechanism enables automatic propagation of trading data changes to asset valuations without requiring complex direct updates, thereby reducing processing time and system complexity.
2Productivity
If traditional batch processing methods are used for asset valuation updates, then system complexity is reduced, but real-time market responsiveness deteriorates
Solution Approach 1:
The system implements continuous real-time updates of asset attributes through automatic propagation mechanisms. When new trading data arrives, the referential link continuously triggers updates to asset valuations without interruption, ensuring the system responds immediately to market changes while maintaining manageable complexity through automated processes.
Solution Approach 2:
The first data structure automatically updates asset attributes using the referential link to the second data structure without requiring manual intervention or complex batch processing schedules. The system serves itself by automatically propagating trading data changes to asset valuations in real-time, improving market responsiveness while keeping processing mechanisms simple.
3Productivity
If multiple data structures are used to separate asset attributes and trading data, then data management efficiency is improved, but the difficulty of maintaining referential integrity increases
Solution Approach 1:
The referential link between the two data structures implements a feedback mechanism where changes in the second data structure (trading data) automatically trigger corresponding updates in the first data structure (asset attributes). This feedback loop maintains referential integrity automatically, reducing the complexity of maintaining data consistency across multiple structures.
4Productivity
If manual order matching processes are used, then system simplicity is maintained, but trading efficiency and liquidity provision deteriorate
Solution Approach 1:
The order matching mechanism operates autonomously using the referential data structures to automatically match buy and sell orders based on real-time asset valuations. The system self-services by continuously monitoring trading data and executing matches without manual intervention, significantly improving trading efficiency and liquidity provision while managing complexity through automated algorithms.
Data Source
AI summary
A server connected to a network generates a database including a first data structure configured to store attributes of fungible assets, where the attributes determine market values of the fungible assets, and a second data structure having fields referentially related to the attributes stored in the first data structure such that a change in any one of the fields induces a change in real time in a corresponding attribute in the first data structure. The server receives a stream of data regarding one or more of the attributes of the fungible assets from the network, which modifies one or more of the fields of the second data structure, and which allows at least one of cycling, liquidating, and replenishing of one or more of the fungible assets while maintaining a relative value equivalence of the fungible assets.