Deep Learning Cardinality Estimation for Skyline Query Variants
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Solution Overview
Problem
Existing cardinality estimation methods for Skyline queries are inefficient, inaccurate, and lack support for various query variants, particularly for reverse Skyline queries, leading to sub-optimal solutions and prolonged processing times.
Innovation Solution
A deep learning-based method that constructs data distribution learning models and cardinality estimation models using historical query logs to predict cardinality estimates, utilizing self-attention transformation networks and neural networks to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical methods (sampling-based, kernel-based, histogram-based) are used for Skyline query cardinality estimation, then the estimation process can be performed for each query object, but the estimation accuracy is sub-optimal and the processing time is very time-consuming when the number of queries is large
Solution Approach 1:
The patent pre-trains data distribution learning models and cardinality estimation models using historical query logs before actual queries arrive. This preliminary training captures data distribution patterns and query characteristics in advance, enabling fast inference during actual query processing without performing time-consuming estimation calculations for each individual query.
Solution Approach 2:
The patent replaces traditional statistical mechanical methods (sampling, kernel density estimation, histogram analysis) with deep learning-based neural network models. The neural networks learn complex patterns from historical data and provide accurate cardinality estimates through learned representations, substituting the mechanical statistical computation process with a more efficient learned model inference process.
2Adaptability or versatility
If traditional cardinality estimation methods are used for Skyline queries, then the estimation can be performed, but the methods are not well supported for Skyline query variants (especially reverse Skyline queries) and lack a general scheme
Solution Approach 1:
The patent designs a universal deep learning-based cardinality estimation framework that can handle multiple Skyline query variants (standard Skyline, reverse Skyline, k-dominant Skyline, k-Skyband, ε-Skyline) through a single unified model architecture. The model learns from historical logs containing various query types and can generalize to estimate cardinality for any of these variants without requiring separate specialized methods for each query type.
3Measurement precision
If deep learning models are trained from scratch for each query type, then the models can be specialized for specific query variants, but the training time and computational resources will be excessive
Solution Approach 1:
The patent performs preliminary training of data distribution learning models and cardinality estimation models using comprehensive historical query logs that contain diverse query types and data patterns. This pre-training establishes a strong foundation of learned representations that can be efficiently adapted or directly applied to specific query variants, avoiding the need to train from scratch for each query type.
Solution Approach 2:
The patent merges the training of data distribution learning and cardinality estimation into a unified deep learning framework. The system combines multiple objectives (data distribution learning, cardinality prediction) into a single end-to-end training process using historical query logs, which improves training efficiency and enables the model to learn joint representations that benefit all query variants simultaneously.
Data Source
AI summary
A cardinality estimation method for Skyline query based on deep learning comprises: parsing historical query log information of a database to obtain Skyline query on a given target dataset and its corresponding cardinality to construct a training set; constructing and training respective data distribution learning models according to distribution information of the target dataset and the training set; using model parameters of the trained data distribution learning models as initialization parameter of the cardinality estimation model, and training the cardinality estimation model according to the training set; inputting query points to obtain final cardinality estimates according to the trained cardinality estimation model. The present disclosure provides a solution for cardinality estimation for Skyline query variants, and ensures the monotonic nature of cardinality estimation for Skyline query variants, and proposes an efficient and accurate cardinality estimation method.