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"Efficient Window Aggregation with General Stream Slicing" Paper Accepted on EDBT 2019

Efficient WindowAggregation with General Stream Slicing, Jonas Traub, Philipp Grulich, Alejandro Rodríguez Cuéllar, Sebastian Breß1, Asterios Katsifodimos, Tilmann Rabl1, Volker Markl . To be presented on 22nd International Conference on Extending Database Technology (EDBT), Lisbon, Portugal, 2019.

Windowaggregation 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., invertible, associative), window types (e.g., sliding, sessions), windowing measures (e.g., time- or countbased), and stream (dis)order. Violating the assumptions of a technique
can deem it unusable or drastically reduce its performance.
In this paper, we present the first general stream slicing technique for window aggregation. General stream slicing automatically adapts to workload characteristics to improve performance without sacrificing its general applicability. As a prerequisite, we identify workload characteristics which affect the performance and applicability of aggregation techniques. Our experiments show that general stream slicing outperforms alternative concepts by up to one order of magnitude.

Download the paper here (PDF)

If you want to learn more about the conference, please, follow the link: EDBT/ICDT 2019 Joint Conference