DataGarage: Warehousing Massive Performance Data on Commodity Servers
Summary: DataGarage warehouses years of performance telemetry from tens of thousands of servers on commodity hardware. Its hybrid DBMS/filesystem/MapReduce architecture addresses the scale and complexity that defeat conventional database and filesystem solutions. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Charles Loboz (Microsoft)
- 2. Slawek Smyl (Microsoft)
- 3. Suman Nath (Microsoft)
BibTeX Citation
@article{loboz_vldb10,
title = {{DataGarage: Warehousing Massive Performance Data on Commodity Servers}},
author = {Loboz, Charles and Smyl, Slawek and Nath, Suman},
journal = {PVLDB},
series = {{VLDB} '10},
volume = {3},
number = {2},
pages = {1447--1458},
doi = {10.14778/1920841.1921022},
url = {https://doi.org/10.14778/1920841.1921022},
year = {2010}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,764 | Fast Approximate Correlation for Massive Time-series Data | 2010 | SIGMOD | 9.8120315e-05 |
| 4,234 | GRAIL: Efficient Time-Series Representation Learning | 2019 | VLDB | 6.8171563e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6 | Pig Latin: A Not-So-Foreign Language for Data Processing | 2008 | SIGMOD | 0.0010686205 |
| 30 | SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets | 2008 | VLDB | 0.00051174276 |
| 44 | A Comparison of Approaches to Large-Scale Data Analysis | 2009 | SIGMOD | 0.00046055057 |
| 60 | Integrating Compression and Execution in Column-Oriented Database Systems | 2006 | SIGMOD | 0.0003955489 |
| 120 | HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads | 2009 | VLDB | 0.00031680027 |
| 1,764 | Fast Approximate Correlation for Massive Time-series Data | 2010 | SIGMOD | 9.8120315e-05 |
| 4,882 | Managing Massive Time Series Streams with Multi-Scale Compressed Trickles | 2009 | VLDB | 6.4651572e-05 |
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