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  1. Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '16)
  2. Mining frequent trajectory patterns from online footprints
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Formalization of network-constrained moving object queries with application to benchmarking
Continuous detection of black holes for moving objects at sea
Categorizing spatiotemporal aggregates for moving regions
On computing temporal functions for time-dependent networks using trajectory data streams
ST-DCONTOUR: a serial, density-contour based spatio-temporal clustering approach to cluster location streams
A survey of techniques and open-source tools for processing streams of spatio-temporal events
A general feature-based map matching framework with trajectory simplification
MobiDict: a mobility prediction system leveraging realtime location data streams
Mining frequent episodes from multivariate spatiotemporal event sequences
Mining frequent trajectory patterns from online footprints

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Mining frequent trajectory patterns from online footprints

Content Provider ACM Digital Library
Author Huang, Qunying Li, Zhenlong Chang, Charles Li, Jing
Abstract Trajectory pattern mining has been performed over many datasets, including animal movement, GPS trajectories, and human travel history. This paper aims to explore and mine individual frequently visited regions and trajectory patterns using online footprints captured through a social media site (i.e., Twitter). Regions of frequent visits representing daily activity areas at which an individual appears are derived using the DBSCAN clustering algorithm. A trajectory pattern mining algorithm is then applied to discover ordered sequences of these spatial regions that the individual visits frequently. To illustrate and test the effectiveness of the proposed methods, we analyze the activity patterns of a selected Twitter user using the geo-tagged tweets posted by the user for an extended period. The preliminary assessment indicates that our approach can be applied to mine individual frequent trajectory patterns from online footprints that are of relatively low and irregular spatial and temporal resolutions.
Starting Page 1
Ending Page 7
Page Count 7
File Format PDF
ISBN 9781450345798
DOI 10.1145/3003421.3003431
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-10-31
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Spatial data mining Spatiotemporal clustering Spatial clustering Sequential pattern Social network Big data Giscience Human mobility Pattern analysis
Content Type Text
Resource Type Article
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