The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Mining Individual Life Pattern Based on Location History
Abstract
The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enables people to conveniently log their location history into spatial-temporal data, thus giving rise to the necessity as well as opportunity to discovery valuable knowledge from this type of data. In this paper, we propose the novel notion of individual life pattern, which captures individual's general life style and regularity. Concretely, we propose the life pattern normal form (the LP-normal form) to formally describe which kind of life regularity can be discovered from location history; then we propose the LP-Mine framework to effectively retrieve life patterns from raw individual GPS data. Our definition of life pattern focuses on significant places of individual life and considers diverse properties to combine the significant places. LP-Mine is comprised of two phases: the modelling phase and the mining phase. The modelling phase pre-processes GPS data into an available format as the input of the mining phase. The mining phase applies separate strategies to discover different types of pattern. Finally, we conduct extensive experiments using GPS data collected by volunteers in the real world to verify the effectiveness of the framework.
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
Cited by
The promises of big data and small data for travel behavior (aka human mobility) analysis
Chen, C., Ma, J., Susilo, Y., Liu, Y., & Wang, M. (2016) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2016.04.005 · Cited by 580
Applying mobile phone data to travel behaviour research: A literature review
Wang, Z., He, S. Y., & Leung, Y. (2018) · Travel Behaviour and Society · doi:10.1016/j.tbs.2017.02.005 · Cited by 367
Analysis and visualisation of movement: an interdisciplinary review
Demšar, U., Buchin, K., Cagnacci, F., Safi, K., Speckmann, B., Van de Weghe, N., Weiskopf, D., & Weibel, R. (2015) · Movement Ecology · doi:10.1186/s40462-015-0032-y · Cited by 128
Metadata
- Authors
- Ye, Y., Zheng, Y., Chen, Y., Feng, J., & Xie, X.
- Venue
- 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
- Publisher
- IEEE
- Published
- 2009
- Pages
- 1-10
- OpenAlex
- W2109182426
- Cited by
- 275
- References
- 1
- FWCI
- 25.20
- 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: Mining Individual Life Pattern Based on Location History. https://app.cochat.ai/papers/10.1109/mdm.2009.11
- The paper
- Ye, Y., Zheng, Y., Chen, Y., Feng, J., & Xie, X. (2009). Mining Individual Life Pattern Based on Location History. 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware. https://doi.org/10.1109/mdm.2009.11
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.
