Distributed Query Prediction for Edge Database Preloading
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Solution Overview
Problem
Current approaches to managing value chain networks are limited by centralized data collection due to bandwidth, storage, processing, and other limitations, leading to overwhelmed systems and missed opportunities for insight and timely decision-making.
Innovation Solution
A method for processing queries in a distributed database, where an edge device receives queries, stores them on a dynamic ledger, detects summary data, generates an approximate response based on this data, and transmits it back, optimizing data processing and reducing network overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If centralized data collection is used to manage value chain networks, then comprehensive data access is achieved, but system bandwidth, storage, and processing become overwhelmed
Solution Approach 1:
The patent segments the centralized database into multiple distributed edge devices, each storing local copies of data. This allows organizations to access comprehensive data across the value chain without overwhelming a single centralized system, as data is distributed across multiple nodes that can independently process queries.
Solution Approach 2:
The patent introduces a new dimension to data management by implementing hierarchical data storage with summary data at edge devices and detailed data at source systems. This multi-layered approach enables comprehensive data access while distributing the processing burden across different levels of the hierarchy.
2Loss of information
If all detailed data is transmitted to centralized systems for analysis, then complete information is available, but network bandwidth and transmission time increase
Solution Approach 1:
The patent extracts essential summary data from detailed source data and stores it at edge devices. This extraction allows organizations to obtain the necessary information for decision-making without transmitting all detailed data across the network, significantly reducing bandwidth requirements and transmission time.
Solution Approach 2:
The patent performs preliminary data processing at edge devices by pre-computing and storing summary statistics, aggregates, and key metrics before queries are executed. This preliminary action ensures that when queries are received, organizations can quickly retrieve pre-processed summary data without requiring time-consuming transmission and processing of all detailed source data.
3Productivity
If summary data is used for quick responses, then processing speed increases, but measurement precision decreases
Solution Approach 1:
The patent implements a dynamic query response mechanism that automatically selects between summary data and detailed data based on query requirements. For time-sensitive queries, summary data provides quick responses, while for queries requiring high precision, the system dynamically retrieves detailed data from source systems, ensuring both speed and accuracy are optimized according to specific needs.
4Loss of energy
If distributed edge devices process queries locally, then network overhead is reduced, but data consistency across devices becomes more difficult to maintain
Solution Approach 1:
The patent implements feedback mechanisms where edge devices periodically synchronize their local summary data with source systems and each other. This feedback loop ensures that distributed edge devices maintain consistent data across the network, with automatic updates propagating changes from source systems to all edge devices, thereby maintaining data consistency while preserving the benefits of distributed processing.
Data Source
AI summary
A computer-implemented method for optimizing a distributed database includes receiving, at an aggregator, one or more query logs comprising past queries received by the distributed database. The method includes generating, by the aggregator, a query prediction model based on the one or more query logs. The method includes predicting, by the aggregator, a future query using the query prediction model. The predicted future query is predicted to be received by an edge device. The method includes causing, by the aggregator, data for responding to the predicted future query to be transmitted to the edge device.