In the Land of Data Streams where Synopses are Missing, One Framework to Bring Them All

Summary: Condor makes synopsis-based streaming a first-class operator by modeling synopses as windowed aggregate functions. Divide-and-conquer distribution enables linear-scale, high-performance processing, achieving up to 75x speedups over prior approaches. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12554
Venue: VLDB
Year: 2021
Pagerank: 5.093636e-05
Overall Rank: 11,700 | 19.73%
DOI: 10.14778/3467861.3467871

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Rudi Poepsel-Lemaitre (Technical University of Berlin)
2. Martin Kiefer (Technical University of Berlin)
3. Joscha von Hein (Technical University of Berlin)
4. Jorge-Arnulfo Quiané-Ruiz (German National Research Center for Information Technology; Technical University of Berlin)
5. Volker Markl (German National Research Center for Information Technology; Technical University of Berlin)

BibTeX Citation

@article{poepsellemaitre_vldb21,
        title = {{In the Land of Data Streams where Synopses are Missing, One Framework to Bring Them All}},
        author = {Poepsel-Lemaitre, Rudi and Kiefer, Martin and von Hein, Joscha and Quiané-Ruiz, Jorge-Arnulfo and Markl, Volker},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {10},
        pages = {1818--1831},
        doi = {10.14778/3467861.3467871},
        url = {https://doi.org/10.14778/3467861.3467871},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 19 of 19 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
26	Models and Issues in Data Stream Systems	2002	PODS	0.00052982574
35	Improved Histograms for Selectivity Estimation of Range Predicates	1996	SIGMOD	0.00048481081
36	Accurate Estimation Of The Number Of Tuples Satisfying A Condition	1984	SIGMOD	0.00048351457
361	The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing	2015	VLDB	0.00020138717
363	Approximate Query Processing: Taming the TeraBytes! A Tutorial	2001	VLDB	0.0002005475
451	Mergeable Summaries	2012	PODS	0.00018151445
671	Load Shedding in a Data Stream Manager	2003	VLDB	0.00015154516
819	Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters	2016	SIGMOD	0.00013815639
885	Finding Frequent Items in Data Streams	2008	VLDB	0.00013419017
1,045	Sketching Streams Through the Net: Distributed Approximate Query Tracking	2005	VLDB	0.00012440928
1,354	On-the-Fly Sharing for Streamed Aggregation	2006	SIGMOD	0.00011057534
1,703	Semantics and Evaluation Techniques for Window Aggregates in Data Streams	2005	SIGMOD	9.9673138e-05
2,455	DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees	2019	VLDB	8.5552968e-05
2,868	SnappyData: A Unified Cluster for Streaming, Transactions, and Interactive Analytics	2017	CIDR	8.0156936e-05
3,351	RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! -	2018	VLDB	7.4937347e-05
4,188	One-Pass Wavelet Synopses for Maximum-Error Metrics	2005	VLDB	6.8448865e-05
4,229	On Biased Reservoir Sampling in the Presence of Stream Evolution	2006	VLDB	6.8181027e-05
8,059	Probabilistic Histograms for Probabilistic Data	2009	VLDB	5.4979799e-05
8,878	Scotch: Generating FPGA-Accelerators for Sketching at Line Rate	2021	VLDB	5.3532614e-05

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