Selection Query Handles for Fast Multidimensional Data Operations
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Solution Overview
Problem
Existing data management systems face inefficiencies in handling large datasets and performing complex queries, particularly in multi-dimensional data structures, leading to high computational costs and slow query performance.
Innovation Solution
The implementation of a selection query language and an associative data indexing engine that generates handles for data subsets, allowing for efficient operations on hypercubes and reducing the need for traditional SQL traversal, combined with bidirectional table and associative indexes to manage associations among data points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional SQL traversal is used to query large datasets in multi-dimensional data structures, then query capability is maintained, but computational cost increases and query performance decreases
Solution Approach 1:
The patent segments data into multi-dimensional hypercubes with orthogonal dimensions, allowing queries to traverse only relevant segments rather than entire datasets. The handle structure divides data into manageable chunks that can be independently processed, reducing computational overhead while maintaining query capability.
Solution Approach 2:
The patent introduces a handle dimension that represents the selection state of data elements. By operating on handles rather than raw data, the system adds a dimensional layer that enables efficient query processing through bitwise operations on handle arrays, significantly reducing computational cost compared to traditional SQL traversal.
2Productivity
If handles are generated for data subsets to enable efficient operations on hypercubes, then query performance improves, but device complexity increases
Solution Approach 1:
The handle acts as an intermediary object between the data storage system and the query processing system. Instead of directly manipulating complex multi-dimensional data structures, the system operates on simplified handle representations that encode selection states, reducing operational complexity while maintaining access to the full data complexity when needed.
Solution Approach 2:
The handle creates a simplified copy or representation of the data selection state rather than copying the actual data. This handle copy contains only the necessary selection information (bitmask or bitvector) and can be manipulated efficiently without duplicating the full multi-dimensional data structure, reducing memory and processing requirements.
3Measurement precision
If bidirectional table indexes are used to manage associations among data points, then data association accuracy improves, but device complexity increases
Solution Approach 1:
The bidirectional index is segmented into forward and reverse index components, each managing associations in opposite directions. The forward index maps from first dimension to second dimension while the reverse index maps from second dimension to first dimension, allowing precise association tracking without requiring a single complex index structure.
Solution Approach 2:
The bidirectional index structure provides symmetric access capabilities in both directions, ensuring that association queries can be efficiently performed regardless of the query direction. This equipotential design simplifies the index logic by treating forward and reverse associations equally, reducing overall structural complexity while maintaining precision.
Data Source
AI summary
Provided are methods and systems comprising receiving a plurality of data tables, wherein each data table comprises at least one row, generating a first handle for a first value in a table of the plurality of tables, wherein the first handle comprises an indication of whether a row in each of the plurality of tables is associated or not associated with the first value, and performing one or more operations on the first handle.