The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Approximate frequency counts over data streams
Abstract
Research in data stream algorithms has blossomed since late 90s. The talk will trace the history of the Approximate Frequency Counts paper, how it was conceptualized and how it influenced data stream research. The talk will also touch upon a recent development: analysis of personal data streams for improving our quality of lives.
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
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Cited by
An improved data stream summary: the count-min sketch and its applications
Cormode, G., & Muthukrishnan, S. (2005) · Journal of Algorithms · doi:10.1016/j.jalgor.2003.12.001 · Cited by 1,743
Frequent pattern mining: current status and future directions
Han, J., Cheng, H., Xin, D., & Yan, X. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-006-0059-1 · Cited by 1,380
A Method of Extracting The Semi-structured Data Implication Rules
Lin, Y., Jun, Z., Hongyan, M., Zhongwei, Z., & Zhanfang, F. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.04.315 · Cited by 19
Metadata
- Authors
- Manku, G. S., & Motwani, R.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2012-08
- Volume
- 5
- Issue
- 12
- Pages
- 1699-1699
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W2069980026
- Cited by
- 1,178
- References
- 1
- FWCI
- 105.51
- Type
- journal article
- 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: Approximate frequency counts over data streams. https://app.cochat.ai/papers/10.14778/2367502.2367508
- The paper
- Manku, G. S., & Motwani, R. (2012). Approximate frequency counts over data streams. Proceedings of the VLDB Endowment. https://doi.org/10.14778/2367502.2367508
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.
