On the algebra of data sketches

Author:

Lemiesz Jakub1

Affiliation:

1. Wrocław University of Science and Technology, Wrocław, Poland

Abstract

We consider the problem of designing a distributed data sketch for scenario in which data stream is observed by many independent network nodes. We require that a sketch apart from being computationally and memory efficient should also be mergeable in a way that mimics set theory operations on related data sets. For example, when monitoring network traffic, one may consider how many distinct packets passed through a given node (sum of sets for different time windows) or passed through two given nodes (intersection of sets from two locations) and what is their total size (intersection of weighted sets). In this paper we propose a sketch that allows to efficiently summarize sets constructed from a sequence of set theory operations. We also provide an analytical control over the trade-off between the accuracy and storage/computational requirements. In comparison to the previous works the proposed solution 1) allows the weights of elements, 2) allows performing set theory operations simultaneous on a large number of sketches, 3) does not require computationally expensive numerical calculations and guarantees low overheads.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. QSketch: An Efficient Sketch for Weighted Cardinality Estimation in Streams;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Pb-Hash: Partitioned b-bit Hashing;Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval;2024-08-02

3. A Revisit to Graph Neighborhood Cardinality Estimation;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

4. FastSO: A Fast Weighted Cardinality Estimation Algorithm;2023 3rd International Conference on Electronic Information Engineering and Computer (EIECT);2023-11-17

5. OmniSketch: Efficient Multi-Dimensional High-Velocity Stream Analytics with Arbitrary Predicates;Proceedings of the VLDB Endowment;2023-11

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