OTIF: Efficient Tracker Pre-processing over Large Video Datasets

Summary: OTIF pre-processes large video datasets to extract all object tracks in one pass; a joint parameter tuning framework optimizes pre-processing for broad reusability. Outputs are general-purpose tracks enabling multi-query workloads with sub-second latencies, delivering 6x–25x reductions over five queries versus prior video query optimizers. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6350
Venue: SIGMOD
Year: 2022
Pagerank: 6.7750533e-05
Overall Rank: 4,298 | 70.52%
DOI: 10.1145/3514221.3517835

Incoming Non-self Citations Over Time

Authors

1. Favyen Bastani (Massachusetts Institute of Technology)
2. Samuel Madden (Massachusetts Institute of Technology)

BibTeX Citation

@inproceedings{bastani_sigmod22,
        title = {{OTIF: Efficient Tracker Pre-processing over Large Video Datasets}},
        author = {Bastani, Favyen and Madden, Samuel},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517835},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517835},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,220	Databases Unbound: Querying All of the World’s Bytes with AI	2024	VLDB	6.3108362e-05
6,494	Extract-Transform-Load for Video Streams	2023	VLDB	5.8639535e-05
8,362	EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions	2023	VLDB	5.4432545e-05
8,829	Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees	2026	SIGMOD	5.3613784e-05
9,144	Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations	2024	VLDB	5.315132e-05
9,924	TVM: A Tile-based Video Management Framework	2024	VLDB	5.1955087e-05
9,925	DoveDB: A Declarative and Low-Latency Video Database	2023	VLDB	5.1955087e-05
10,623	KEN: An Execution Engine for Unstructured Database Systems	2026	VLDB	5.093636e-05
10,791	High-Throughput Ingestion for Video Warehouse: Comprehensive Configuration and Effective Exploration	2025	SIGMOD	5.093636e-05
10,917	Deja Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse	2025	VLDB	5.093636e-05
11,162	Predictive and Near-Optimal Sampling for View Materialization in Video Databases	2024	SIGMOD	5.093636e-05
11,268	Optimizing Video Queries with Declarative Clues	2024	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
284	NoScope: Optimizing Neural Network Queries over Video at Scale	2017	VLDB	0.00022370521
295	Accelerating Machine Learning Inference with Probabilistic Predicates	2018	SIGMOD	0.00022238183
1,042	MIRIS: Fast Object Track Queries in Video	2020	SIGMOD	0.00012451966
1,607	Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine	2019	CIDR	0.0001022751

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