Subset Sampling over Joins

2025-12-18

AI Summary

This work addresses the problem of efficient independent subset sampling over join results—drawing a subset of join tuples independently with prescribed probabilities, without explicitly materializing the exponentially large join output. To tackle this challenge, we propose the first suite of algorithms for acyclic joins, comprising static index construction, single-shot sampling execution, and dynamic index maintenance. Our framework uniformly supports weighted sampling under product-, sum-, and extremum-based decomposition functions, as well as real-time data insertion. We introduce join-aware sampling theory and index structure optimizations that achieve near-optimal time and space complexity. Experimental evaluation demonstrates that our approach enables multiple rounds of independent sampling and dynamic updates, reducing computational overhead significantly compared to materialization-based sampling.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Data Mining & Knowledge Management: Intelligent Query Processing
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web crawling and indexing
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Subset sampling (also known as Poisson sampling), where the decision to include any specific element in the sample is made independently of all others, is a fundamental primitive in data analytics, enabling efficient approximation by processing representative subsets rather than massive datasets. While sampling from explicit lists is well-understood, modern applications -- such as machine learning over relational data -- often require sampling from a set defined implicitly by a relational join. In this paper, we study the problem of emph{subset sampling over joins}: drawing a random subset from the join results, where each join result is included independently with some probability. We address the general setting where the probability is derived from input tuple weights via decomposable functions (e.g., product, sum, min, max). Since the join size can be exponentially larger than the input, the naive approach of materializing all join results to perform subset sampling is computationally infeasible. We propose the first efficient algorithms for subset sampling over acyclic joins: (1) a emph{static index} for generating multiple (independent) subset samples over joins; (2) a emph{one-shot} algorithm for generating a single subset sample over joins; (3) a emph{dynamic index} that can support tuple insertions, while maintaining a one-shot sample or generating multiple (independent) samples. Our techniques achieve near-optimal time and space complexity with respect to the input size and the expected sample size.

Problem

• Efficiently sampling subsets from relational join results
• Avoiding materialization of exponentially large join outputs
• Supporting dynamic data updates while maintaining sampling efficiency

Innovation

• Efficient algorithms for subset sampling over acyclic joins
• Static index for multiple independent subset samples over joins
• Dynamic index supporting tuple insertions while maintaining samples