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2019•Unpublished venueOpen access

Generating Reproducible Out-of-Order Data Streams

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

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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.

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What this paper is about

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.

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Available 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.

Key concepts: Tuple, Data stream mining, Computer science, STREAMS, Data stream, Generator (circuit theory), Stream processing, Data mining

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