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Toward Progress Indicators on Steroids for Big Data Systems

Summary: Proposes a 'predict–monitor–revise' paradigm to deliver continuously updated progress and completion-time predictions for big-data jobs, resilient to skew, resource contention, and failures. Advocates principled fusion of runtime traces, cost models, and telemetry to yield accurate, updatable indicators for schedulers and users. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
211
Venue
CIDR
Year
2013
Pagerank
4.5250912e-05
Overall Rank
8,401 | 41.56%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
5,844 MIFO: A Query-Semantic Aware Resource Allocation Policy 2019 SIGMOD 5.3030037e-05
6,345 Operator and Query Progress Estimation in Microsoft SQL Server Live Query Statistics 2016 SIGMOD 5.1023048e-05
7,913 Resource Bricolage for Parallel Database Systems 2015 VLDB 4.6180739e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank Cited Paper Year Venue Pagerank
3 Pig Latin: A Not-So-Foreign Language for Data Processing 2008 SIGMOD 0.0024183614
1,228 Toward a Progress Indicator for Database Queries 2004 SIGMOD 0.00013164884
1,334 SkewTune: Mitigating Skew in MapReduce Applications 2012 SIGMOD 0.0001250413
1,770 ParaTimer: A Progress Indicator for MapReduce DAGs 2010 SIGMOD 0.00010618229
2,241 Query Optimization in Microsoft SQL Server PDW 2012 SIGMOD 9.2191212e-05
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