Accelerating Aggregation Queries on Unstructured Streams of Data

Summary: InQuest: streaming, multimodal aggregation over unstructured data using cheap proxy models plus sampling to limit expensive oracle invocations, producing real-time approximate query answers with statistical guarantees. Theory: expected error on stationary streams decays ∝1/(oracle budget); evaluation: matches streaming baselines with up to 5× fewer oracle calls and improves RMSE vs a state-of-the-art batch method. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: hf2e3282f69e395c0
Venue: VLDB
Year: 2023
Pagerank: 5.4507311e-05
Overall Rank: 7,801 | 47.56%
DOI: 10.14778/3611479.3611496

Incoming Non-self Citations Over Time

Authors

1. Matthew Russo (Stanford University)
2. Tatsunori Hashimoto (Stanford University)
3. Daniel Kang (University of Illinois Urbana-Champaign)
4. Yi Sun (University of Chicago)
5. Matei Zaharia (Stanford University)

BibTeX Citation

@article{russo_vldb23,
        title = {{Accelerating Aggregation Queries on Unstructured Streams of Data}},
        author = {Russo, Matthew and Hashimoto, Tatsunori and Kang, Daniel and Sun, Yi and Zaharia, Matei},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {11},
        pages = {2897--2910},
        doi = {10.14778/3611479.3611496},
        url = {https://doi.org/10.14778/3611479.3611496},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,126	Abacus: A Cost-Based Optimizer for Semantic Operator Systems	2026	VLDB	7.6185225e-05
8,223	PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees	2025	SIGMOD	5.3751366e-05
8,997	Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees	2026	SIGMOD	5.2410834e-05
10,144	Deep Research is the New Analytics System: Towards Building the Runtime for AI-Driven Analytics	2026	CIDR	5.0715586e-05
10,690	Task Cascades for Efficient Unstructured Data Processing	2026	SIGMOD	4.9793485e-05
11,079	Efficient Approximate Query Processing with Block Sampling	2025	CIDR	4.9793485e-05
11,111	MAST: Towards Efficient Analytical Query Processing on Point Cloud Data	2025	SIGMOD	4.9793485e-05
11,210	Scalable Complex Event Processing on Video Streams	2025	SIGMOD	4.9793485e-05
11,310	Deja Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse	2025	VLDB	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
36	Accurate Estimation Of The Number Of Tuples Satisfying A Condition	1984	SIGMOD	0.00047863192
271	NoScope: Optimizing Neural Network Queries over Video at Scale	2017	VLDB	0.00022560564
541	BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics	2020	VLDB	0.00016657685
852	Streaming Queries over Streaming Data	2002	VLDB	0.00013451343
1,024	MIRIS: Fast Object Track Queries in Video	2020	SIGMOD	0.00012430491
2,710	Panorama: A Data System for Unbounded Vocabulary Querying over Video	2020	VLDB	8.1041775e-05
2,776	Approximate Selection with Guarantees using Proxies	2020	VLDB	8.0309448e-05
3,219	Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics	2021	VLDB	7.5207996e-05
3,583	STREAM: The Stanford Stream Data Manager (Demonstration Description)	2003	SIGMOD	7.1908304e-05
3,650	TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data	2022	SIGMOD	7.1341771e-05
3,874	Accelerating Approximate Aggregation Queries with Expensive Predicates	2021	VLDB	6.953738e-05
4,324	On Biased Reservoir Sampling in the Presence of Stream Evolution	2006	VLDB	6.6656407e-05
4,495	Evaluating Temporal Queries Over Video Feeds	2021	SIGMOD	6.5769374e-05

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