The Case for Small Data Management
Summary: Argues most real-world DB workloads are small (K–M rows), yet research and systems optimize for massive scale, causing inefficient plans and excess complexity. Presents research directions and PDbF (Portable Database Files) as a practical redesign for storage, optimization, and tooling tailored to small-data. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Jens Dittrich (Saarland University)
BibTeX Citation
@inproceedings{dittrich_cidr15,
address = {Amsterdam, Netherlands},
series = {{CIDR} '15},
title = {{The Case for Small Data Management}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Dittrich, Jens},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 12,142 | Janiform Intra-Document Analytics for Reproducible Research | 2015 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,054 | Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads | 2012 | VLDB | 0.00012390673 |
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