Generating reproducible out-of-order data streams

Philipp M. Grulich; Jonas Traub; Sebastian Bress; Asterios Katsifodimos; Volker Markl; Tilmann Rabl

doi:10.1145/3328905.3332511

Generating reproducible out-of-order data streams

Philipp M. Grulich, Jonas Traub, Sebastian Bress, Asterios Katsifodimos, Volker Markl, Tilmann Rabl

Web Information Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

Evaluating modern stream processing systems in a reproducible manner requires data streams with different data distributions, data rates, and real-world characteristics such as delayed and out-of-order tuples. In this paper, we present an open source stream generator which generates reproducible and deterministic out-of-order streams based on real data files, simulating arbitrary fractions of out-of-order tuples and their respective delays.

Original language	English
Title of host publication	DEBS'19
Subtitle of host publication	Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	256-257
Number of pages	2
ISBN (Electronic)	978-1-4503-6794-3
DOIs	https://doi.org/10.1145/3328905.3332511
Publication status	Published - 24 Jun 2019
Event	13th ACM International Conference on Distributed and Event-Based Systems, DEBS 2019 - Darmstadt, Germany Duration: 24 Jun 2019 → 28 Jun 2019

Conference

Conference	13th ACM International Conference on Distributed and Event-Based Systems, DEBS 2019
Country	Germany
City	Darmstadt
Period	24/06/19 → 28/06/19

Keywords

Benchmarking
Data Generation
Out-of-Order
Stream Processing

Access to Document

10.1145/3328905.3332511

Grulich Reproducible out of order streams paperFinal published version, 3.74 MB

Link to publication in Scopus

Cite this

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title = "Generating reproducible out-of-order data streams",

abstract = "Evaluating modern stream processing systems in a reproducible manner requires data streams with different data distributions, data rates, and real-world characteristics such as delayed and out-of-order tuples. In this paper, we present an open source stream generator which generates reproducible and deterministic out-of-order streams based on real data files, simulating arbitrary fractions of out-of-order tuples and their respective delays.",

keywords = "Benchmarking, Data Generation, Out-of-Order, Stream Processing",

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Grulich, PM, Traub, J, Bress, S, Katsifodimos, A, Markl, V & Rabl, T 2019, Generating reproducible out-of-order data streams. in DEBS'19 : Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems. Association for Computing Machinery (ACM), New York, pp. 256-257, 13th ACM International Conference on Distributed and Event-Based Systems, DEBS 2019, Darmstadt, Germany, 24/06/19. https://doi.org/10.1145/3328905.3332511

Generating reproducible out-of-order data streams. / Grulich, Philipp M.; Traub, Jonas; Bress, Sebastian; Katsifodimos, Asterios; Markl, Volker; Rabl, Tilmann.

DEBS'19 : Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems. New York : Association for Computing Machinery (ACM), 2019. p. 256-257.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Rabl, Tilmann

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