Data Aggregation System with Checkpoint Recovery
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Solution Overview
Problem
Existing systems fail to dynamically aggregate data from multiple networked systems with different data types and formats in real-time, and are unable to resume data collection after unexpected events like network outages, leading to data loss and inefficiencies in decision-making.
Innovation Solution
A computer-implemented system and method that aggregates data from multiple networked systems by collecting and transforming data into master data structures using relational maps and transformation rules, allowing for near real-time data access and minimizing data loss by resuming data collection from the last successful aggregation point after system failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data aggregation is performed from multiple networked systems with different data types and formats, then data comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The patent introduces a data aggregation system that acts as an intermediary between multiple networked systems with different data types and formats. This intermediary system collects data from various sources, transforms diverse data formats into a unified structure using transformation rules, and reconciles data into a proprietary format. This resolves the contradiction by managing the complexity centrally rather than requiring each system to handle all data format conversions individually.
Solution Approach 2:
The system dynamically changes data parameters by applying transformation rules that convert different data types and formats into a standardized proprietary format. The transformation rules modify data parameters (format, structure, encoding) to enable uniform processing and analysis across all networked systems, thereby improving data comprehensiveness without proportionally increasing system complexity.
2Loss of time
If real-time data collection is implemented across multiple systems, then decision-making speed is improved, but vulnerability to data loss from network outages increases
Solution Approach 1:
The patent implements beforehand cushioning by establishing checkpoint mechanisms that track the last successful aggregation point across multiple networked systems. Before attempting real-time data collection, the system prepares by identifying these checkpoint positions, so that if a network outage occurs, data loss is minimized by resuming from the last known good state rather than losing all accumulated data. This cushioning mechanism protects against data loss while maintaining real-time collection capabilities.
Solution Approach 2:
The system employs feedback mechanisms to monitor the status of data collection across networked systems. When a network outage or system failure is detected, the feedback loop triggers a recovery process that resumes data aggregation from the last successful checkpoint. This feedback-driven approach allows the system to maintain real-time data collection speed while automatically correcting for data loss events, thereby improving both decision-making speed and reliability.
3Quantity of substance
If data aggregation resumes after system failures, then data completeness is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing checkpoint markers that record the last successful aggregation point before any failure occurs. When a system failure happens, the recovery process doesn't need to reprocess all data from scratch but can resume from the pre-marked checkpoint position. This preliminary action of marking checkpoints significantly reduces the processing time required to achieve data completeness after failures, as the system only needs to process data from the checkpoint forward rather than reprocessing the entire data set.
4Adaptability or versatility
If diverse data formats are reconciled into a unified format, then data analysis capability is improved, but transformation complexity increases
Solution Approach 1:
The patent implements universality by creating a proprietary unified data format that can represent multiple different source data formats. The transformation rules are designed to be multi-functional, handling various data types (structured, unstructured, semi-structured) and formats through a single transformation framework. This universal approach improves data analysis capability by providing a consistent target format for all sources while managing transformation complexity through a standardized, multi-purpose transformation engine rather than requiring separate transformation logic for each data type.
Data Source
AI summary
A computer-implemented system for dynamic aggregation of data and minimization of data loss is disclosed. The system may be configured to perform instructions for: aggregating information from a plurality of networked systems by collecting a set of data from the networked systems, the set of data comprising data associated with a predetermined period of time and comprising one or more central variables that are included in data associated with more than one networked systems of the plurality of networked systems and one or more associated variables that describe one or more aspects of the central variables; retrieving one or more data transformation rules based on a relational map among the central variables and the associated variables; and aggregating the first set of data into one or more master data structures corresponding to the central variables based on the data transformation rules.