The DOI matches its Crossref record.
Models and issues in data stream systems
Abstract
In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.
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
Cited by
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735
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
Mining concept-drifting data streams using ensemble classifiers
Wang, H., Fan, W., Yu, P. S., & Han, J. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956778 · Cited by 1,256
Data stream clustering: A survey
Silva, J. A., Faria, E. R., Barros, R. C., Hruschka, E. R., Carvalho, A. C. P. L. F. D., & Gama, J. (2013) · ACM Computing Surveys · doi:10.1145/2522968.2522981 · Cited by 508
A catalog of stream processing optimizations
Hirzel, M., Soulé, R., Schneider, S., Gedik, B., & Grimm, R. (2014) · ACM Computing Surveys · doi:10.1145/2528412 · Cited by 299
Metadata
- Authors
- Babcock, B., Babu, S., Datar, M., Motwani, R., & Widom, J.
- Venue
- Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
- Publisher
- ACM
- Published
- 2002-06-03
- Pages
- 1-16
- OpenAlex
- W2001474264
- Cited by
- 2,495
- References
- 0
- FWCI
- 69.51
- 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: Models and issues in data stream systems. https://app.cochat.ai/papers/10.1145/543613.543615
- The paper
- Babcock, B., Babu, S., Datar, M., Motwani, R., & Widom, J. (2002). Models and issues in data stream systems. Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. https://doi.org/10.1145/543613.543615
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.
