The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Improved histograms for selectivity estimation of range predicates
Abstract
Many commercial database systems maintain histograms to summarize the contents of relations and permit efficient estimation of query result sizes and access plan costs. Although several types of histograms have been proposed in the past, there has never been a systematic study of all histogram aspects, the available choices for each aspect, and the impact of such choices on histogram effectiveness. In this paper, we provide a taxonomy of histograms that captures all previously proposed histogram types and indicates many new possibilities. We introduce novel choices for several of the taxonomy dimensions, and derive new histogram types by combining choices in effective ways. We also show how sampling techniques can be used to reduce the cost of histogram construction. Finally, we present results from an empirical study of the proposed histogram types used in selectivity estimation of range predicates and identify the histogram types that have the best overall performance. 1 Introduction...
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Kolmogorov-Smirnov Test for Goodness of Fit
Massey, F. J. (1951) · Journal of the American Statistical Association · doi:10.1080/01621459.1951.10500769 · Cited by 7,210
Cited by
Space-efficient online computation of quantile summaries
Greenwald, M., & Khanna, S. (2001) · Proceedings of the 2001 ACM SIGMOD international conference on Management of data · doi:10.1145/375663.375670 · Cited by 451
Worst-case optimal join algorithms: [extended abstract]
Ngo, H. Q., Porat, E., Ré, C., & Rudra, A. (2012) · Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems · doi:10.1145/2213556.2213565 · Cited by 324
Approximate medians and other quantiles in one pass and with limited memory
Manku, G. S., Rajagopalan, S., & Lindsay, B. G. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276342 · Cited by 261
Metadata
- Authors
- Poosala, V., Haas, P. J., Ioannidis, Y. E., & Shekita, E. J.
- Venue
- Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96
- Publisher
- ACM Press
- Published
- 1996
- Pages
- 294-305
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2171903035
- Cited by
- 556
- References
- 1
- FWCI
- 19.83
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Improved histograms for selectivity estimation of range predicates. https://app.cochat.ai/papers/10.1145/233269.233342
- The paper
- Poosala, V., Haas, P. J., Ioannidis, Y. E., & Shekita, E. J. (1996). Improved histograms for selectivity estimation of range predicates. Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96. https://doi.org/10.1145/233269.233342
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
