Searched for: author%3A%22Traub%2C+Jonas%22
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Grulich, Philipp M. (author), Traub, Jonas (author), Bress, Sebastian (author), Katsifodimos, A (author), Markl, Volker (author), Rabl, Tilmann (author)
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...
conference paper 2019
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Traub, Jonas (author), Grulich, Philipp (author), Cuéllar, Alejandro Rodríguez (author), Breß, Sebastian (author), Katsifodimos, A (author), Rabl, Tilmann (author), Markl, Volker (author)
Window aggregation is a core operation in data stream processing. Existing aggregation techniques focus on reducing latency, eliminating redundant computations, and minimizing memory usage. However, each technique operates under different assumptions with respect to workload characteristics such as properties of aggregation functions (e.g.,...
conference paper 2019
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Traub, Jonas (author), Grulich, Philipp Marian (author), Rodriguez Cuellar, Alejandro (author), Bress, Sebastian (author), Katsifodimos, A (author), Rabl, Tilmann (author), Markl, Volker (author)
Computing aggregates over windows is at the core of virtually every stream processing job. Typical stream processing applications involve overlapping windows and, therefore, cause redundant computations. Several techniques prevent this redundancy by sharing partial aggregates among windows. However, these techniques do not support out-of...
conference paper 2018