Learned Query Optimization for Random Access in Compacted Data
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Solution Overview
Problem
Existing random access systems for compacted data files suffer from performance bottlenecks due to inaccurate location estimation, boundary detection failures, and inefficient search strategies that do not adapt to user behavior and data characteristics, leading to extensive searching operations and reactive operation without proactive caching.
Innovation Solution
An adaptive random access system with learned query optimization that incorporates machine learning to analyze historical access patterns, improve location estimation accuracy, predict boundaries, and implement proactive caching, using a query pattern learning module, adaptive estimator module, predictive boundary detector, intelligent search engine, and dynamic codebook optimizer to enhance data retrieval efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression methods are used to reduce storage demand, then storage capacity efficiency is improved, but random access capability deteriorates because the entire dataset must be decompressed first
Solution Approach 1:
The patent divides the compressed data into discrete sourceblocks that can be independently accessed. Each sourceblock is a self-contained unit that can be decompressed and retrieved without processing the entire compressed dataset, enabling random access while maintaining compression efficiency.
Solution Approach 2:
The system pre-processes data into sourceblocks with embedded reference codebooks during the compression phase. This preliminary organization allows the random access engine to directly locate and decompress specific sourceblocks without full decompression, preparing the data structure in advance for efficient random access.
2Device complexity
If simple mathematical estimation is used for location determination, then system complexity is reduced, but location estimation accuracy deteriorates leading to extensive searching operations
Solution Approach 1:
The patent implements a feedback mechanism where the random access engine uses reference codebooks to verify and refine location estimates. The system checks whether estimated locations contain valid sourceblock structures, providing feedback that confirms or corrects location accuracy without requiring exhaustive searching.
Solution Approach 2:
The patent replaces brute-force mechanical searching with an intelligent estimation system that uses mathematical calculations combined with structural verification. Instead of linearly scanning through compressed data, the system calculates probable locations and validates them using reference codebook patterns, substituting computational intelligence for mechanical search.
3Device complexity
If static frequency analysis is used for boundary detection, then algorithm simplicity is maintained, but boundary detection accuracy deteriorates for complex data patterns
Solution Approach 1:
The patent transitions from static frequency analysis to dynamic pattern recognition for boundary detection. The system adapts its detection strategy based on the specific characteristics of the compressed data being analyzed, using learned patterns from reference codebooks to dynamically adjust boundary detection thresholds and methods for different data types and compression levels.
4Device complexity
If uniform search strategies are used regardless of query characteristics, then system complexity is reduced, but search efficiency deteriorates
Solution Approach 1:
The patent applies different search strategies tailored to specific query characteristics and data types. Rather than using a uniform approach, the system selects optimal search methods based on the local properties of the query - such as whether it seeks text, binary data, or structured information - and the characteristics of the compressed sourceblocks, thereby optimizing search efficiency for each specific case.
5Device complexity
If reactive operation is used where each query is processed independently, then system simplicity is maintained, but overall performance deteriorates due to lack of optimization
Solution Approach 1:
The patent implements continuous learning and optimization where the system accumulates information from each query and uses it to improve subsequent operations. The random access engine maintains state about accessed sourceblocks, query patterns, and performance metrics, continuously refining its search and retrieval strategies to improve overall system performance beyond what any single isolated query could achieve.
Data Source
AI summary
An adaptive random access system and method with learned query optimization for compacted data files that enhances random access performance through machine learning and pattern recognition. The system incorporates a query pattern learning module that analyzes historical access patterns and user behavior to build statistical models of data usage. An adaptive estimator module improves location estimation accuracy by incorporating learned patterns rather than relying solely on mathematical calculations. A predictive boundary detector uses learned codeword patterns to more accurately identify boundaries in compacted data, reducing misalignment errors. An intelligent search engine coordinates optimization strategies including context-aware search string parsing and encoding strategy selection based on learned performance data. A dynamic codebook optimizer reorganizes sourceblock layout based on access frequencies and co-occurrence patterns to improve retrieval speed. An enhanced search cache implements predictive caching algorithms that anticipate user queries and proactively load relevant data.