Dynamic Data Index Restructure for BI Query Optimization
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Solution Overview
Problem
Business intelligence architectures face performance issues due to slow data retrieval from disk storage, which is exacerbated by increased data demands and limited available non-volatile memory in in-memory databases, leading to inefficiencies and high costs.
Innovation Solution
A dynamic data index restructure system that uses predictive crowdsourcing to anticipate and optimize data access patterns by restructing data indexes based on usage patterns, employing a predictive engine and heuristic engine to pre-generate data likely to be requested, thereby reducing the need for disk access and optimizing query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If in-memory databases load entire datasets into non-volatile memory to improve query speed, then query performance is improved, but memory requirements increase significantly and hardware costs increase
Solution Approach 1:
The system performs preliminary actions by predicting which data will be needed and pre-loading only that specific data into non-volatile memory, rather than loading entire datasets. The predictive engine analyzes query patterns and pre-loads predicted data before it is actually needed, reducing memory requirements while maintaining fast query performance.
Solution Approach 2:
Instead of uniformly loading all data into memory, the system applies local quality by selectively loading only the specific data portions that are predicted to be needed based on query patterns. This allows different parts of the database to be treated differently - frequently accessed data is loaded into memory while less frequently accessed data remains on disk.
2Productivity
If pre-processing data to retrieve smaller numbers of records is implemented, then data retrieval speed is improved, but accurate predictions of data selection and arrangement are required which increases system complexity
Solution Approach 1:
The system implements self-service by having the predictive engine automatically learn from actual query patterns and adjust its predictions without requiring manual configuration or complex prediction algorithms. The engine continuously refines its understanding of data access patterns, reducing the complexity burden on the system while maintaining high prediction accuracy.
3Quantity of substance
If additional non-volatile memory is added to increase data capacity, then data storage capacity is improved, but hardware cost increases significantly
Solution Approach 1:
The system uses preliminary action through predictive pre-loading to maximize the utilization of existing non-volatile memory capacity. By predicting which data will be needed and loading only that data, the system reduces the amount of memory required, thereby reducing hardware costs while maintaining adequate data storage capacity for the intended workload.
Data Source
AI summary
A system, method, and computer-readable medium for performing a dynamic data index restructure operation. The dynamic data index restructure operation restructures the data indexes within a business intelligence architecture based on usage patterns so as to support queries most likely to be generated by users of the business intelligence architecture. In certain embodiments, the dynamic data index restructure operation is performed via a dynamic data index restructure agent which may be positioned between a business intelligence application and the business intelligence database.