Real-Time Financial Data Processing System with Dynamic Adaptation
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Solution Overview
Problem
Current systems for processing and distributing financial data streams are labor-intensive, computationally expensive, and lack scalability, requiring hardwiring to specific data structures and formats, making them difficult to integrate and adapt to multiple data sources and client types, especially with the rise of portable and wireless devices with varying bandwidths.
Innovation Solution
A system comprising a real-time information manager and client manager that processes raw financial data streams to produce structured and validated output, capable of handling multiple streams in real-time, dynamically adjusting transmission rates, and aggregating data changes for efficient distribution to clients, while allowing easy adaptation to new data sources and types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate ad-hoc systems are used to process data from different sources, then each system can be optimized for its specific data structure, but the overall system complexity increases and scalability is reduced
Solution Approach 1:
The patent implements a universal data processing system with a standardized interface layer that can handle multiple data sources and formats. The system uses a common data model and processing framework that works across different data types (treasuries, corporate offerings, etc.), eliminating the need for separate ad-hoc systems while maintaining the ability to process each data type effectively through configuration rather than structural modification.
Solution Approach 2:
The system is divided into distinct modular components: data intake layer, standardized interface layer, processing engine, and distribution layer. Each component has a specific function and can be independently configured for different data types, allowing the system to maintain simplicity while handling diverse data sources through modular assembly rather than monolithic complexity.
2Productivity
If the host is hardwired to specific data structures and formats, then processing for that specific source is optimized, but adaptability to new data sources and types is reduced
Solution Approach 1:
The system employs dynamic configuration capabilities where data processing parameters, validation rules, and transformation logic can be adjusted at runtime without modifying the underlying system architecture. This allows the system to maintain optimized processing for existing data sources while adapting to new data types through configuration changes, achieving both efficiency and versatility.
Solution Approach 2:
The patent implements a parameter-driven processing model where data source characteristics are defined through configurable parameters rather than hard-coded structures. When new data sources are introduced, only their specific parameters need to be defined, allowing the core processing engine to handle them efficiently using the same optimized routines, thus maintaining productivity while gaining adaptability.
3Speed
If high-speed data connections are used for data transmission, then data delivery speed is improved, but system robustness to network inconsistencies is reduced
Solution Approach 1:
The system implements buffering and error correction mechanisms at multiple levels: data validation before processing, checkpointing during processing, and verification before distribution. These preemptive measures cushion against network inconsistencies and data corruption, allowing the system to maintain high-speed transmission while protecting against errors that might occur over unreliable networks.
Solution Approach 2:
The patent incorporates feedback loops where the system monitors data transmission status, validates received data integrity, and adjusts transmission parameters dynamically. This feedback mechanism allows the system to maintain optimal speed while automatically detecting and correcting errors, thereby achieving both high-speed transmission and robustness to network inconsistencies.
Data Source
AI summary
A real time information manager which processes raw data from data providers, such as feeds of current information about financial product offerings, is provided. Raw data objects received from various data providers are analyzed, formatted, and a current version of each unique data object is stored in an object storage pool. Information about the state of each objects is transmitted on a particular output data channel in accordance with the type of object. A subscriber process can access one or more of the data channels and utilize the broadcast data. In one configuration, one or more client managers are provided which subscribe to particular data channels and serve as an intermediary between the real time information manager and sets of clients.