Multi-dimensional Data Statistics for Columnar In-Memory Databases
Summary: Introduces multi-dimensional statistics for columnar in-memory DBs using an order-preserving, dense-domain dictionary per column. Fixed-size encoding maps variable-length values to dense dictionary entries, yielding compact storage and enabling efficient encoded-data statistics for cross-dimensional queries. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Curtis Kroetsch (University of Waterloo)
- 2. Anisoara Nica (SAP)
BibTeX Citation
@inproceedings{kroetsch_sigmod14,
title = {{Multi-dimensional Data Statistics for Columnar In-Memory Databases}},
author = {Kroetsch, Curtis and Nica, Anisoara},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2612663},
url = {https://dl.acm.org/doi/10.1145/2588555.2612663},
year = {2014}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,954 | SAP HANA: The Evolution from a Modern Main-Memory Data Platform to an Enterprise Application Platform | 2013 | VLDB | 6.9026723e-05 |
| 5,815 | Exploiting Ordered Dictionaries to Efficiently Construct Histograms with Q-Error Guarantees in SAP HANA | 2014 | SIGMOD | 5.9844993e-05 |
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