UniGrid Index Structure for Uncertain Data Query Processing
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Solution Overview
Problem
Existing query processing systems face challenges in indexing high-dimensional and uncertain data, as traditional distance functions are ineffective and sensitive to dimensionality, leading to sparsity issues and loss of contrast in similarity calculations.
Innovation Solution
The UniGrid index structure is introduced, which uses a two-level inverted partitioning based on mean values and uncertainty values, employing expected distance functions and probabilistic proximity functions to efficiently handle queries in high-dimensional spaces by focusing on dimensions with probabilistic closeness to thresholds, optimizing distance function design and indexing for uncertain data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional distance functions are used for high-dimensional uncertain data, then the system can process queries, but the distance functions become ineffective due to sparsity and loss of contrast
Solution Approach 1:
The patent segments the high-dimensional uncertain data into multiple clusters using clustering algorithms. Each cluster represents a group of similar uncertain data points, reducing the effective dimensionality and density of the search space. This segmentation prevents the sparsity problem by organizing data into manageable groups where distance functions remain effective.
Solution Approach 2:
The patent applies different processing strategies to different parts of the data structure. The indexing structure uses probabilistic thresholds and uncertainty-aware distance calculations specifically tailored for uncertain data regions, while deterministic indexing methods are used for certain data. This local adaptation maintains distance function effectiveness in high-dimensional spaces.
2Productivity
If traditional indexing structures are used for uncertain data, then the system can store data, but query processing becomes inefficient due to sensitivity to dimensionality
Solution Approach 1:
The patent performs preliminary clustering and indexing of uncertain data before query processing. The data is pre-organized into clusters with computed centroids and uncertainty boundaries, allowing queries to quickly eliminate large portions of the data space without performing complex distance calculations on all data points.
Solution Approach 2:
The patent introduces a new dimensional organization by clustering data points in high-dimensional space into discrete cluster groups. This transforms the continuous high-dimensional search space into a discrete hierarchical structure, adding a new organizational dimension that facilitates efficient query processing without being constrained by the original high dimensionality.
3Measurement precision
If direct extensions of Lq-metric are used, then the system can compute distances, but the functions become qualitatively ineffective due to sparsity in high dimensional spaces
Solution Approach 1:
The patent segments the high-dimensional space into clusters, reducing the effective number of dimensions that need to be considered for distance computation. Within each cluster, the distance function operates on a smaller, denser subset of dimensions, maintaining measurement precision without being affected by the sparsity of the full high-dimensional space.
Solution Approach 2:
The patent extracts and focuses on the most relevant dimensions for distance computation within each cluster, ignoring dimensions that contribute primarily to sparsity. This selective extraction of important dimensions maintains distance computation accuracy while reducing the impact of high dimensionality.
Data Source
AI summary
Techniques are disclosed for indexing uncertain data in query processing systems. For example, a method for processing queries in an application that involves an uncertain data set includes the following steps. A representation of records of the uncertain data set is created based on mean values and uncertainty values. The representation is utilized for processing a query received on the uncertain data set.