Referential Data Structures for Real-Time Asset Attribute Updates
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Solution Overview
Problem
Existing systems fail to efficiently and automatically update asset attributes in real-time based on streaming data, leading to inefficiencies in market value determination and trading processes, particularly in handling fungible assets across multiple baskets 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 supporting Sharia-compliant trading through a weighted average benchmark and secure, tokenized transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional database structures are used to store asset attributes, then data storage is simple, but real-time automatic updates of asset attributes based on streaming data cannot be achieved
Solution Approach 1:
The database is divided into two separate data structures: a first data structure storing asset attributes and a second data structure storing streaming data. This segmentation allows independent optimization of each structure - the first structure maintains simple attribute storage while the second structure handles real-time data updates, resolving the contradiction between update accuracy and structural complexity.
Solution Approach 2:
A processor acts as an intermediary between the two data structures, receiving streaming data in the second structure and automatically updating corresponding attributes in the first structure. This intermediary mechanism enables real-time automatic updates without requiring complex direct linking between structures, maintaining simplicity while achieving real-time accuracy.
2Productivity
If manual updating of asset attributes is used, then system complexity is low, but market value determination efficiency deteriorates
Solution Approach 1:
The system implements self-service automation where the processor automatically updates asset attributes by matching streaming data with existing asset records. The first data structure is automatically updated based on incoming data in the second structure without manual intervention, enabling real-time market value determination while maintaining straightforward update logic through automated data matching and comparison.
Solution Approach 2:
The system establishes a feedback loop where streaming data continuously updates asset attributes, which in turn update market values. The processor monitors incoming data, automatically adjusts attributes based on predefined rules, and recalculates market values in real-time. This automated feedback mechanism dramatically improves productivity while the rule-based approach keeps automation complexity manageable.
3Measurement precision
If asset attributes are updated frequently based on streaming data, then market value accuracy is improved, but system processing load increases
Solution Approach 1:
The system performs preliminary actions by pre-structuring the database with two dedicated data structures and pre-defining update rules in the processor. Asset attributes are organized in the first structure with clear schemas, and update logic is predetermined. This preliminary preparation enables efficient real-time updates without requiring complex processing during each update cycle, reducing energy consumption while maintaining high accuracy through structured data flow and predefined update mechanisms.
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 field induces a change in real time in a corresponding attribute in the first data structure. The server receives a stream of data regarding the attributes of the fungible assets from the network, modifies the fields of the second data structure, and allows cycling, liquidating, and replenishing of one or more of the fungible assets while maintaining a relative value equivalence of the fungible assets. The server securely validated, verifies, records, traces, and tracks transactions for cryptocurrency specimens. The server generates tokens based on the fungible assets for trading over subnetworks and provides price discovery.