Mining positional data streams
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New Frontiers in Mining Complex Patterns. Springer, 2015. p. 102-116.
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Mining positional data streams
AU - Haase, Jens
AU - Brefeld, Ulf
N1 - Conference code: 3
PY - 2015
Y1 - 2015
N2 - We study frequent pattern mining from positional data streams. Existing approaches require discretised data to identify atomic events and are not applicable in our continuous setting. We propose an efficient trajectory-based preprocessing to identify similar movements and a distributed pattern mining algorithm to identify frequent trajectories. We empirically evaluate all parts of the processing pipeline.
AB - We study frequent pattern mining from positional data streams. Existing approaches require discretised data to identify atomic events and are not applicable in our continuous setting. We propose an efficient trajectory-based preprocessing to identify similar movements and a distributed pattern mining algorithm to identify frequent trajectories. We empirically evaluate all parts of the processing pipeline.
KW - Informatics
KW - Pattern Mining
KW - Dynamic Time Warping
KW - Positional Data
KW - Frequent Episode
KW - Event Stream
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=84931056379&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-17876-9_7
DO - 10.1007/978-3-319-17876-9_7
M3 - Article in conference proceedings
AN - SCOPUS:84931056379
SN - 978-3-319-17875-2
SP - 102
EP - 116
BT - New Frontiers in Mining Complex Patterns
PB - Springer
T2 - 3rd International Workshop on New Frontiers in Mining Complex Patterns - NFMCP 2014
Y2 - 19 September 2014 through 19 September 2014
ER -