The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A survey of temporal knowledge discovery paradigms and methods
Abstract
With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining.
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 association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Maintaining knowledge about temporal intervals
Allen, J. F. (1983) · Communications of the ACM · doi:10.1145/182.358434 · Cited by 7,593 · cited 2×
“Notable work includes that of Dean and McDermott [21], [22] who developed inter alia , a comprehensive system for reasoning about time, and Allen [16], Vilain [18], [23], and Freksa [17], who refined the temporal relationships used in much of the temporal data mining research.”
Background
Cited by
Keeping the Resident in the Loop: Adapting the Smart Home to the User
Rashidi, P., & Cook, D. J. (2009) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2009.2025137 · Cited by 443
Metadata
- Authors
- Roddick, J. F., & Spiliopoulou, M.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2002-07
- Volume
- 14
- Issue
- 4
- Pages
- 750-767
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2122354896
- Cited by
- 397
- References
- 2
- FWCI
- 51.86
- 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: A survey of temporal knowledge discovery paradigms and methods. https://app.cochat.ai/papers/10.1109/tkde.2002.1019212
- The paper
- Roddick, J. F., & Spiliopoulou, M. (2002). A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2002.1019212
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.
