Sparse Delta Model for Brokerage Transaction Data
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Solution Overview
Problem
Current systems for processing transactions in brokerage operations are inefficient and costly due to the need to maintain data records for all days, including those with no activity, and struggle to scale with large volumes of transactions, especially when handling forward-dated and back-dated transactions.
Innovation Solution
The system employs a Sparse Delta Model (SDM) that processes transactions by setting activity records with specific dates, accounting impact dates, and journal activity dates, allowing for efficient retrieval of 'as-on' or 'as-of' values by summing balance values and adjustments, and archiving records according to a retention schedule to reduce database size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data records are maintained for all days including those with no activity, then complete historical data is available for querying, but storage capacity requirements increase significantly
Solution Approach 1:
The patent extracts only the necessary data (transaction records with activity) from the complete time period, eliminating unnecessary data for days with no activity. This allows the system to maintain historical data completeness for querying while significantly reducing storage capacity requirements by storing only actual transaction records rather than all daily records.
Solution Approach 2:
The patent segments the data into discrete transaction records organized by date, allowing selective retrieval of data for specific dates or date ranges. This segmentation enables efficient querying of historical data without requiring continuous storage of all days, as only relevant segments (days with transactions) need to be maintained and retrieved.
2Stability of the object's composition
If all data from previous day is copied and carried forward, then data continuity is maintained, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing aggregate data (daily balances, position changes) at the time of transaction processing. This eliminates the need for copying and carrying forward all detailed transaction data from previous days, as the aggregated results are already prepared and can be directly retrieved for queries, significantly improving processing speed while maintaining data continuity.
Solution Approach 2:
The patent creates simplified copies of data in the form of aggregate records that capture essential information (balances, positions) without copying all detailed transaction records. These summarized copies maintain data continuity for queries while reducing the volume of data that needs to be processed and stored, thereby improving processing speed.
3Measurement precision
If forward-dated and back-dated transactions are processed by updating all intervening dates, then accurate balance information is achieved, but processing complexity and costs increase
Solution Approach 1:
The patent extracts the core function of balance calculation by maintaining running totals of transactions in a simplified structure. Instead of updating all intervening dates with detailed transaction records, the system stores only the net change and final balance state, achieving accurate balance information with reduced processing complexity and lower computational costs.
Solution Approach 2:
The patent changes the representation of balance data from detailed daily transaction records to aggregated parameters (net changes, final balances). This parameter transformation allows the system to maintain measurement precision for balance accuracy while significantly reducing processing complexity, as operations can be performed on the aggregated parameters rather than individual transaction records for each date.
4Loss of information
If data records are maintained for all days, then complete query results are available, but system scalability is limited
Solution Approach 1:
The patent segments data into discrete, indexable transaction records organized by date, enabling efficient retrieval for any date or date range. This segmentation structure allows the system to scale horizontally by adding more records without degrading query performance, as data can be selectively retrieved based on date filters rather than requiring all records to be stored and processed simultaneously.
Solution Approach 2:
The patent introduces a temporal dimension to data organization by sorting and indexing records by date, allowing queries to efficiently navigate through time-based data. This dimensional organization enables complete query results to be retrieved through indexed searches rather than scanning all records, significantly improving system scalability for handling large volumes of historical data.
Data Source
AI summary
Systems and techniques for operating on data representing an account include posting an activity record representing account activity. The systems and techniques may involve setting an activity date for an activity having an accounting impact, determining a balance value, setting an accounting impact date, and setting a journal activity date. The balance value may correspond to the net sum of activity for the activity date. The accounting impact date may correspond to the latest accounting impact date among all journals affecting the balance value. The journal activity date may correspond to the latest accounting impact date among all journals affecting the sum of activity for prior processing dates applied on the date. In some cases, the activity record may be posted to a specific period.