The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Computing longest duration flocks in trajectory data
Abstract
Moving point object data can be analyzed through the discovery of patterns. We consider the computational efficiency of computing two of the most basic spatio-temporal patterns in trajectories, namely flocks and meetings. The patterns are large enough subgroups of the moving point objects that exhibit similar movement and proximity for a certain amount of time. We consider the problem of computing a longest duration flock or meeting. We give several exact and approximation algorithms, and also show that some variants are as hard as MaxClique to compute and approximate.
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
Clique is hard to approximate within n1−ε
Håstad, J. (1999) · Acta Mathematica · doi:10.1007/bf02392825 · Cited by 1,369
Geographic Data Mining and Knowledge Discovery
Miller, H. J. (2007) · The Handbook of Geographic Information Science · doi:10.1002/9780470690819.ch19 · Cited by 617
Clique is hard to approximate within n/sup 1-ε/
Hastad, J. (2002) · Proceedings of 37th Conference on Foundations of Computer Science · doi:10.1109/sfcs.1996.548522 · Cited by 458
On Discovering Moving Clusters in Spatio-temporal Data
Kalnis, P., Mamoulis, N., & Bakiras, S. (2005) · Lecture Notes in Computer Science · doi:10.1007/11535331_21 · Cited by 420
Mining, indexing, and querying historical spatiotemporal data
Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., & Cheung, D. W. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014080 · Cited by 289
Efficient Detection of Patterns in 2D Trajectories of Moving Points
Gudmundsson, J., van Kreveld, M. J., & Speckmann, B. (2007) · GeoInformatica · doi:10.1007/s10707-006-0002-z · Cited by 117
Self-citationLearning spatio-temporal patterns for predicting object behaviour
Sumpter, N., & Bulpitt, A. J. (2000) · Image and Vision Computing · doi:10.1016/s0262-8856(99)00073-6 · Cited by 97
Life and Motion of Socio-Economic Units
Frank, A. U., Raper, J. F., & Cheylan, J.-P. (2000) · Taylor & Francis eBooks · doi:10.4324/9780203305706 · Cited by 93
Supporting fast search in time series for movement patterns in multiple scales
Qu, Y., Wang, C., & Wang, X. S. (1998) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/288627.288664 · Cited by 76
On approximating the depth and related problems
Aronov, B. S., & Har-Peled, S. (2005) · doi:10.5555/1070432.1070556 · Cited by 43
Cited by
Urban Computing: Concepts, Methodologies, and Applications
Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2629592 · Cited by 1,507
Discovery of convoys in trajectory databases
Jeung, H., Yiu, M. L., Zhou, X., Jensen, C. S., & Shen, H. T. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1453856.1453971 · Cited by 431
A model for enriching trajectories with semantic geographical information
Alvares, L. O., Bogorny, V., Kuijpers, B., de Macedo, J. A. F., Moelans, B., & Vaisman, A. (2007) · Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems · doi:10.1145/1341012.1341041 · Cited by 405
Towards a taxonomy of movement patterns
Dodge, S., Weibel, R., & Lautenschütz, A.-K. (2008) · Information Visualization · doi:10.1057/palgrave.ivs.9500182 · Cited by 344
Unveiling the complexity of human mobility by querying and mining massive trajectory data
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F., Renso, C., Rinzivillo, S., & Trasarti, R. (2011) · The VLDB Journal · doi:10.1007/s00778-011-0244-8 · Cited by 287
Metadata
- Authors
- Gudmundsson, J., & van Kreveld, M.
- Venue
- Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
- Publisher
- ACM
- Published
- 2006-11-10
- Pages
- 35-42
- OpenAlex
- W2033626772
- Cited by
- 277
- References
- 10
- FWCI
- 11.24
- 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: Computing longest duration flocks in trajectory data. https://app.cochat.ai/papers/10.1145/1183471.1183479
- The paper
- Gudmundsson, J., & van Kreveld, M. (2006). Computing longest duration flocks in trajectory data. Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems. https://doi.org/10.1145/1183471.1183479
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.
