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AStream: Ad-hoc Shared Stream Processing
Summary: First distributed ad-hoc shared stream processing framework on Apache Flink, enabling multi-tenant ad-hoc queries to share computation. Composable integration for joins, aggregations, and windows with exactly-once, incremental execution; yields high throughput for many concurrent queries (comparable to Flink for one query, orders of magnitude better for many).
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h4ebde4e37c7d5cd0
Venue
SIGMOD
Year
2019
Pagerank
5.7187564e-05
Overall Rank
6,607 | 55.72%
DOI
10.1145/3299869.3319884
Incoming Non-self Citations Over Time
Authors
1.
Jeyhun Karimov
(German National Research Center for Information Technology)
2.
Tilmann Rabl
(German National Research Center for Information Technology; Technical University of Berlin)
3.
Volker Markl
(German National Research Center for Information Technology; Technical University of Berlin)
BibTeX Citation
Copy BibTeX
@inproceedings{karimov_sigmod19,
title = {{AStream: Ad-hoc Shared Stream Processing}},
author = {Karimov, Jeyhun and Rabl, Tilmann and Markl, Volker},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3319884},
url = {https://dl.acm.org/doi/10.1145/3299869.3319884},
year = {2019}
}
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Showing 28 of 28 cited papers.
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Rank
Cited Paper
Year
Venue
Pagerank
1
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1979
SIGMOD
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23
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2015
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53
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2000
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201
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2002
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233
Storm @Twitter
2014
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0.00023754096
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2012
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The DataPath System: A Data-Centric Analytic Processing Engine for Large Data Warehouses
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Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark
2018
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BatchDB: Efficient Isolated Execution of Hybrid OLTP+OLAP Workloads for Interactive Applications
2017
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2006
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2017
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On the Design and Scalability of Distributed Shared-Data Databases
2015
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2005
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1,926
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2009
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2,237
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2014
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2003
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Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems
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2013
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2007
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2016
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2015
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OLTPShare: The Case for Sharing in OLTP Workloads
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