Supporting Scalable Analytics with Latency Constraints
Summary: Latency-aware analytics for big- and fast-data via mini-batch compute and shuffle. Model-driven resource allocation and scheduling to meet latency with throughput; Incremental Hadoop delivers sub-second latency and 2–5× throughput, beating Storm/Spark. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Boduo Li (University of Massachusetts Amherst)
- 2. Yanlei Diao (University of Massachusetts Amherst)
- 3. Prashant Shenoy (University of Massachusetts Amherst)
BibTeX Citation
@article{li_vldb15,
title = {{Supporting Scalable Analytics with Latency Constraints}},
author = {Li, Boduo and Diao, Yanlei and Shenoy, Prashant},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {11},
pages = {1166--1177},
doi = {10.14778/2809974.2809984},
url = {https://doi.org/10.14778/2809974.2809984},
year = {2015}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,902 | UDAO: A Next-Generation Unified Data Analytics Optimizer | 2019 | VLDB | 5.1997534e-05 |
| 11,360 | Templating Shuffles | 2023 | CIDR | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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