Estimating the Size of Union of Sets in Streaming Models

Meel, Kuldeep S.; Vinodchandran, N. V.; Chakraborty, Sourav

doi:10.1145/3452021.3458333

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…One line of work in this area is on DNF counting problem [20], including designing hashing-based algorithms [9,14,21,28]. Another popular line of work is on estimating the union of sets where each set arrives in a streaming fashion [6,19,27,29].…”

Section: Related Workmentioning

confidence: 99%

Sampling over Union of Joins

Liu¹,

Yunlong²,

Nargesian³

2023

Preprint

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Data scientists often draw on multiple relational data sources for analysis. A standard assumption in learning and approximate query answering is that the data is a uniform and independent sample of the underlying distribution. To avoid the cost of join and union, given a set of joins, we study the problem of obtaining a random sample from the union of joins without performing the full join and union. We present a general framework for random sampling over the set union of chain, acyclic, and cyclic joins, with sample uniformity and independence guarantees. We study the novel problem of union of joins size evaluation and propose two approximation methods based on histograms of columns and random walks on data. We propose an online union sampling framework that initializes with cheap-to-calculate parameter approximations and refines them on the fly during sampling. We evaluate our framework on workloads from the TPC-H benchmark and explore the trade-off of the accuracy of union approximation and sampling efficiency.

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Section: Related Workmentioning

confidence: 99%

Sampling over Union of Joins

Liu¹,

Yunlong²,

Nargesian³

2023

Preprint

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Model Counting meets F ₀ Estimation

Pavan

Vinodchandran

Bhattacharya

et al. 2021

Proceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

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On the Feasibility of Forgetting in Data Streams

Pavan,

Chakraborty,

Vinodchandran

et al. 2024

Proc. ACM Manag. Data

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In today's digital age, it is becoming increasingly prevalent to retain digital footprints in the cloud indefinitely. Nonetheless, there is a valid argument that entities should have the authority to decide whether their personal data remains within a specific database or is expunged. Indeed, nations across the globe are increasingly enacting legislation to uphold the "Right To Be Forgotten" for individuals. Investigating computational challenges, including the formalization and implementation of this notion, is crucial due to its relevance in the domains of data privacy and management. This work introduces a new streaming model: the 'Right to be Forgotten Data Streaming Model' (RFDS model). The main feature of this model is that any element in the stream has the right to have its history removed from the stream. Formally, the input is a stream of updates of the form (a, Δ) where Δ ∈ {+, ⊥} and a is an element from a universe U. When the update Δ=+ occurs, the frequency of a, denoted as f a , is incremented to f a +1. When the update Δ=⊥, occurs, f a is set to 0. This feature, which represents the forget request, distinguishes the present model from existing data streaming models. This work systematically investigates computational challenges that arise while incorporating the notion of the right to be forgotten. Our initial considerations reveal that even estimating F 1 (sum of the frequencies of elements) of the stream is a non-trivial problem in this model. Based on the initial investigations, we focus on a modified model which we call α-RFDS where we limit the number of forget operations to be at most α fraction. In this modified model, we focus on estimating F 0 (number of distinct elements) and F 1 . We present algorithms and establish almost-matching lower bounds on the space complexity for these computational tasks.

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Estimating the Size of Union of Sets in Streaming Models

Cited by 4 publications

References 52 publications

Sampling over Union of Joins

Sampling over Union of Joins

Model Counting meets F ₀ Estimation

On the Feasibility of Forgetting in Data Streams

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Estimating the Size of Union of Sets in Streaming Models

Cited by 4 publications

References 52 publications

Sampling over Union of Joins

Sampling over Union of Joins

Model Counting meets F 0 Estimation

On the Feasibility of Forgetting in Data Streams

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Product

Resources

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Model Counting meets F ₀ Estimation