Multidimensional Data Storage Optimization via Query Pattern Analysis
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Solution Overview
Problem
Existing data storage methods are inefficient in handling multidimensional data, as they require separate normalization for each query, consume time and bandwidth, and are limited to five normal forms, failing to adapt dynamically to user demands and access patterns, and do not provide fast access to similar queries.
Innovation Solution
An optimization system that analyzes query patterns to identify data dimensionality, segmenting or fragmenting data and columns based on 11 characterized dimensions, storing data in remote locations when necessary, and archiving data based on usage patterns to improve access efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate normalization is carried out for each query, then data organization adapts to specific query requirements, but time and bandwidth are consumed repeatedly for the same query patterns
Solution Approach 1:
The system performs preliminary analysis of query patterns to identify recurring access patterns before data retrieval occurs. By pre-processing queries to detect patterns and pre-organizing data accordingly, the system eliminates repeated normalization operations for identical query types, reducing execution time while maintaining adaptability.
Solution Approach 2:
The patent creates a universal data organization structure that can serve multiple query patterns simultaneously. Instead of normalizing data separately for each query type, a single optimized storage structure is designed that efficiently handles families of related queries, making the normalization process universal rather than query-specific.
2Stability of the object's composition
If existing data storage with five normal forms is used, then data is organized in standard relational structures, but the storage cannot be changed dynamically based on user demand and access pattern
Solution Approach 1:
The system implements dynamic data storage structures that can automatically reorganize based on detected query patterns. The storage schema is no longer static but adapts in real-time as the system learns from incoming queries, allowing the data organization to evolve with user demands while maintaining structural integrity through systematic transformation rules.
Solution Approach 2:
The patent incorporates feedback mechanisms where query execution results and access patterns are continuously monitored and fed back into the data organization system. This feedback loop enables the storage structure to automatically adjust its organization based on actual usage patterns, creating a self-optimizing system that balances stability with adaptability.
3Speed
If fast indexing mechanism or factorization mechanism is implemented, then data access speed is improved, but the storage does not provide results based on data demand and usage pattern of different users
Solution Approach 1:
The system applies different storage optimization strategies to different portions of data based on local query patterns. Instead of using a uniform indexing approach for all data, the patent identifies specific data regions accessed by different user groups and organizes each region according to its specific access characteristics, enabling both fast access and user-specific optimization.
Solution Approach 2:
The patent segments the data storage into multiple optimized zones based on query patterns and user requirements. Each segment is organized according to the specific access patterns of the user group that queries it most frequently, allowing the system to deliver user-specific results efficiently while maintaining overall fast access performance across the entire database.
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AI summary
A method for optimizing storage of multi-dimensional data in data storage (113) comprises analyzing (401) a plurality of queries received over period of time from one or more client machines. Then, a query pattern (402) is determined from the plurality of queries. Based on the query pattern, the dimensionality of data is identified (403) for determining a data storage strategy. The dimensionality is characterized into eleven dimensions comprising four standard level dimensions and seven higher level dimensions. At least one of the data and columns of a table is segmented (404) in data storage. Data is stored (406) in remote data storage when data is an element of last higher level dimension. Higher level dimensions are fragmented (409) into one or more smaller level dimensions when data is element greater than eleven dimensions.