Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 992
- Type
- D - Journal article
- DOI
-
10.1109/TKDE.2016.2592527
- Title of journal
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Article number
- -
- First page
- 3098
- Volume
- 28
- Issue
- 11
- ISSN
- 1041-4347
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 38
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work introduces novel and scalable algorithms to identify patterns of human daily behaviours related to smartphone users. The underpinning research involved an international collaboration between 1) Dartmouth College (USA), one of the top US computer science schools, 2) University of Vienna (Austria), the principal centre for teaching and research in computer science and business informatics in Austria, and 3) the University of California-Riverside (USA). This work was included in the ACM Computing Reviews Notable Papers of 2016 (https://hes32-ctp.trendmicro.com:443/wis/clicktime/v1/query?url=http%3a%2f%2fwww.computingreviews.com%2frecommend%2fbestof%2f2016NotableItems.pdf&umid=144a9367-f2ef-492e-bb13-3a0f1d321eba&auth=768f192bba830b801fed4f40fb360f4d1374fa7c-2772a26e5ac64fc593c66b6d9ce97f24f840566d).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
