Author image not provided
 Stef Sijben

Authors:
Add personal information
  Affiliation history
Bibliometrics: publication history
Average citations per article1.00
Citation Count3
Publication count3
Publication years2012-2017
Available for download2
Average downloads per article223.50
Downloads (cumulative)447
Downloads (12 Months)71
Downloads (6 Weeks)24
SEARCH
ROLE
Arrow RightAuthor only


AUTHOR'S COLLEAGUES
See all colleagues of this author

SUBJECT AREAS
See all subject areas




BOOKMARK & SHARE


3 results found Export Results: bibtexendnoteacmrefcsv

Result 1 – 3 of 3
Sort by:

1 published by ACM
December 2017 Journal of Experimental Algorithmics (JEA): Volume 22, 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 22,   Downloads (12 Months): 31,   Downloads (Overall): 31

Full text available: PDFPDF
We introduce the concept of using a flow diagram to compactly represent the segmentation of a large number of state sequences according to a set of criteria. We argue that this flow diagram representation gives an intuitive summary that allows the user to detect patterns within the segmentations. In essence, ...
Keywords: football, Flow networks, group segmentation, sports analytics, experimental, state sequence, network analysis

2
June 2016 SEA 2016: Proceedings of the 15th International Symposium on Experimental Algorithms - Volume 9685
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 0

We introduce the concept of compactly representing a large number of state sequences, e.g., sequences of activities, as a flow diagram. We argue that the flow diagram representation gives an intuitive summary that allows the user to detect patterns among large sets of state sequences. Simplified, our aim is to ...

3 published by ACM
November 2012 SIGSPATIAL '12: Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Publisher: ACM
Bibliometrics:
Citation Count: 3
Downloads (6 Weeks): 2,   Downloads (12 Months): 40,   Downloads (Overall): 416

Full text available: PDFPDF
In trajectory data a low sampling rate leads to high uncertainty in between sampling points, which needs to be taken into account in the analysis of such data. However, current algorithms for movement analysis ignore this uncertainty and assume linear movement between sample points. In this paper we develop a ...
Keywords: geometric algorithms, movement ecology, trajectory



The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2018 ACM, Inc.
Terms of Usage   Privacy Policy   Code of Ethics   Contact Us