Database System for Matching Entities via Segmented Data Marts
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Solution Overview
Problem
Existing database systems lack the ability to efficiently discover related entities and calculate aggregate risks across multiple entities, leading to inefficiencies in data processing and user interface updates, particularly in handling large volumes of data and idiosyncratic risk profiles in transactions.
Innovation Solution
A database system that gathers data from external sources, calculates aggregate values, and matches related entities using customized API requests, processes responses, and provides dynamic interactive graphical user interfaces for efficient data presentation and risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If database systems process large volumes of data to discover related entities and calculate aggregate risks, then measurement precision and reliability improve, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the database into multiple specialized data marts (entity data mart, transaction data mart, risk data mart) that can be processed independently. This segmentation allows parallel processing of different data types and reduces the computational burden on any single processing unit, thereby maintaining measurement precision while reducing overall processing time.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing aggregate risk values, entity relationships, and transaction patterns in the database before they are needed for analysis. This pre-processing enables rapid retrieval and comparison during risk assessment operations, significantly reducing real-time processing time while maintaining accuracy.
2Productivity
If the system calculates aggregate values across multiple entities in real-time, then productivity and decision-making speed improve, but device complexity and computational requirements increase
Solution Approach 1:
The patent implements a universal database schema and processing framework that handles multiple types of calculations (aggregate risks, entity matching, transaction analysis) through a single integrated system. This multi-functional approach consolidates what could be separate complex systems into one unified platform, improving productivity while managing complexity through standardization.
Solution Approach 2:
The system introduces intermediary computational layers including stored procedures, views, and aggregation tables that mediate between raw data and final risk assessments. These intermediaries simplify the complexity by providing standardized interfaces and pre-computed results, allowing the system to handle complex calculations without increasing apparent system complexity to users.
3Loss of information
If the database system stores and processes detailed risk profiles for numerous entities, then information completeness improves, but data storage requirements and processing overhead increase
Solution Approach 1:
The patent extracts only the essential risk-related attributes and relationships from complete entity data, storing them in specialized risk data marts and aggregate tables. This extraction maintains the completeness of risk information needed for analysis while reducing overall storage requirements by eliminating redundant demographic and operational details that are not directly relevant to risk assessment.
Solution Approach 2:
The system applies local quality by storing different levels of detail in different database locations - complete entity profiles are stored in main databases, while risk-specific attributes are stored in specialized data marts, and pre-computed aggregates are stored in summary tables. This allows the system to maintain information completeness where needed while reducing storage overhead in other areas.
Data Source
AI summary
A database system is disclosed for matching related entities. The database system may gather, for example from one or more external data sources, various data items, and optionally calculate aggregate values related to the entities. The database system may further match related entities based on the aggregate values. Data may be gathered via one or more customized requests via specialized application programming interfaces (APIs). The database system may further advantageously receive responses via the specialized APIs, processes and standardize each response, aggregate the responses, and provide the responses for review via dynamic interactive graphical user interfaces.