The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Finding Frequent Items in Data Streams
Overview
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Registry
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Citations
Cites
The Space Complexity of Approximating the Frequency Moments
Alon, N., Matias, Y., & Szegedy, M. (1999) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1545 · Cited by 1,140
Database-friendly random projections
Achlioptas, D. (2001) · Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/375551.375608 · Cited by 699
Computing Iceberg Queries Efficiently
Fang, M., Shivakumar, N., García-Molina, H., Motwani, R., & Ullman, J. D. (1998) · Cited by 340
Fast, small-space algorithms for approximate histogram maintenance
Gilbert, A. C., Guha, S., Indyk, P., Kotidis, Y., Muthukrishnan, S., & Strauss, M. J. (2002) · Proceedings of the thiry-fourth annual ACM symposium on Theory of computing · doi:10.1145/509907.509966 · Cited by 242
Stable distributions, pseudorandom generators, embeddings and data stream computation
Indyk, P. (2002) · Proceedings 41st Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2000.892082 · Cited by 238
Synopsis data structures for massive data sets
Gibbons, P. B., & Matias, Y. (1999) · DIMACS series in discrete mathematics and theoretical computer science · doi:10.1090/dimacs/050/02 · Cited by 189
Testing and Spot-Checking of Data Streams
Feigenbaum, Kannan, Strauss, & Viswanathan, V. (2002) · Algorithmica · doi:10.1007/s00453-002-0959-4 · Cited by 22
Cited by
Dictionary learning for integrative, multimodal and scalable single-cell analysis
Hao, Y., Stuart, T., Kowalski, M. H., Choudhary, S., Hoffman, P., Hartman, A., Srivastava, A., Molla, G., Madad, S., Fernandez-Granda, C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01767-y · Cited by 5,631
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
Fukui, A., Park, D. H., Yang, D., Rohrbach, A., Darrell, T., & Rohrbach, M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1044 · Cited by 1,582
What's hot and what's not: tracking most frequent items dynamically
Cormode, G., & Muthukrishnan, S. (2005) · ACM Transactions on Database Systems · doi:10.1145/1061318.1061325 · Cited by 533
Multimodal intelligence: Representation learning, information fusion, and applications
Zhang, C., Yang, Z., He, X., & Deng, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2987728 · Cited by 511
On the Power of Multiple Anonymous Messages
Ghazi, B., Golowich, N., Kumar, R., Pagh, R., & Velingker, A. (2019) · arXiv · doi:10.48550/arxiv.1908.11358 · Cited by 26
Metadata
- Authors
- Charikar, M., Chen, K., & Farach-Colton, M.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2002
- Pages
- 693-703
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W4249843299
- Cited by
- 650
- References
- 7
- FWCI
- 5.75
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Finding Frequent Items in Data Streams. https://app.cochat.ai/papers/10.1007/3-540-45465-9_59
- The paper
- Charikar, M., Chen, K., & Farach-Colton, M. (2002). Finding Frequent Items in Data Streams. Lecture Notes in Computer Science. https://doi.org/10.1007/3-540-45465-9_59
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.
