Searched for: author%3A%22Rabl%2C+Tilmann%22
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Kunft, Andreas (author), Katsifodimos, A (author), Schelter, Sebastian (author), Bress, Sebastian (author), Rabl, Tilmann (author), Markl, Volker (author)
Machine learning (ML) pipelines for model training and validation typically include preprocessing, such as data cleaning and feature engineering, prior to training an ML model. Preprocessing combines relational algebra and user-defined functions (UDFs), while model training uses iterations and linear algebra. Current systems are tailored to...
journal article 2019
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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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Karimov, Jeyhun (author), Rabl, Tilmann (author), Katsifodimos, A (author), Samarev, Roman (author), Heiskanen, Henri (author), Markl, Volker (author)
The need for scalable and efficient stream analysis has led to the development of many open-source streaming data processing systems (SDPSs) with highly diverging capabilities and performance characteristics. While first initiatives try to compare the systems for simple workloads, there is a clear gap of detailed analyses of the systems'...
conference paper 2018
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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
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Kunft, Andreas (author), Katsifodimos, A (author), Schelter, Sebastian (author), Rabl, Tilmann (author), Markl, Volker (author)
Linear algebra operations are at the core of many Machine Learning (ML) programs. At the same time, a considerable amount of the effort for solving data analytics problems is spent in data preparation. As a result, end-to- end ML pipelines often consist of (i) relational operators used for joining the input data, (ii) user defined functions used...
conference paper 2017
Searched for: author%3A%22Rabl%2C+Tilmann%22
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