Efficient Window Aggregation with General Stream Slicing

Jonas Traub, Philipp Grulich, Alejandro Rodríguez Cuéllar, Sebastian Breß, Asterios Katsifodimos, Tilmann Rabl, Volker Markl

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

31 Citations (Scopus)
623 Downloads (Pure)


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., invertible, associative), window types (e.g., sliding, sessions), windowing measures (e.g., time- or count-based), 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.

Original languageEnglish
Title of host publicationAdvances in Database Technology - EDBT 2019
Subtitle of host publication22nd International Conference on Extending Database Technology, Proceedings
EditorsMelanie Herschel, Helena Galhardas, Carsten Binnig, Zoi Kaoudi, Irini Fundulaki, Berthold Reinwald
Number of pages12
ISBN (Electronic)978-3-89318-081-3
Publication statusPublished - 2019
Event22nd International Conference on Extending Database Technology, EDBT 2019 - Lisbon, Portugal
Duration: 26 Mar 201929 Mar 2019


Conference22nd International Conference on Extending Database Technology, EDBT 2019


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