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Support: Percentage of trajectories which are contained within a pattern

Trajectory pattern mining

Encyclopedia of GIS, pp.330-+, (2007)

被引用1174|浏览163
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摘要

The increasing pervasiveness of location-acquisition tech- nologies (GPS, GSM networks, etc.) is leading to the collec- tion of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and dev...更多

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简介
  • Mechanics

    Data structure Regions of Interest (ROI) T-Patterns using these ROI.
  • Martha Axiak Marco Muscat
  • Nowadays data on the spatial and temporal location is objects is available.
  • Trajectory Pattern Mining: Fosca Giannotti, Micro Nanni, Dino Pedreschi, Fabio Pinelli
  • GSM towers, etc What can the authors do with this data?
  • Data mine it for patterns!!
  • Prediction of movement Aggregate movement behaviour Region of interests discovery Discovery of traffic flow & blockages
  • Data structure Regions of Interest (ROI) T-Patterns using these ROI
  • Multiple Moving Objects
重点内容
  • Mechanics

    Data structure Regions of Interest (ROI) T-Patterns using these Regions of Interest
  • Support: Percentage of trajectories which are contained within a pattern
  • Iteration 2 works on each configurations added in prior iteration
结果
  • Example: Raw input
  • Example: T-Patterns
  • Support: Percentage of trajectories which are contained within a pattern.
  • Example: Support threshold of 0.2.
  • A T-Pattern is kept only if 20% of the trajectories support it.
  • Static pre-processed ROI ROI discovery
  • List of candidate places Dynamic ROI
  • Popular points detection ROI construction
  • Static Dynamic Input: Trajectories and threshold parameters
  • Neighbourhood and time threshold Support threshold
  • Output: T-Patterns
  • T-Pattern Mining (2)
  • Step-wise Heuristic Any frequent T-Pattern of length n+1 is the extension of some frequent T-Pattern of length n.
  • Aim is to find TPatterns.
  • First iteration prefix length is zero.
  • E.g. support(r)
  • For each region find possible projections to other regions
  • Projections: A B
  • With respect to region B.
  • With respect to
  • Add new configuration (T’, )
  • Iteration 2 works on each configurations added in prior iteration.
  • Test projects from each of the regions.
  • E Recompute ROIs.
  • Results in configuration (T’, )
结论
  • (T’’, )
  • Dynamic Discovery of T-Patterns (12)
  • D E No possible projections.
  • Same thing happens for region E.
  • D E No more paths to go stops and outputs paths: B
  • Lack of comparative results Idea of ROI is good for applications looking for simple and course results.
  • Cited many times for ROI discovery algorithm
  • Where : T-Patterns used to predict where a person will move given the current sequence.
  • DAEDALUS: Extends SQL to enable TAS queries.
引用论文
  • F. Giannotti, M. Nanni, and D. Pedreschi. Efficient mining of sequences with temporal annotations http://www.siam.org/meetings/sdm06/proceedings/032giannottif.pdf
    Locate open access versionFindings
  • Monreale, A., Pinelli, F., Trasarti, R., and Giannotti, F. 2009. WhereNext: a location predictor on trajectory pattern mining http://doi.acm.org/10.1145/1557019.1557091
    Findings
  • Ortale, R., Ritacco, E., Pelekis, N., Trasarti, R., Costa, G., Giannotti, F., Manco, G., Renso, C., and Theodoridis, Y. 2008. The DAEDALUS framework: progressive querying and mining of movement data.
    Google ScholarFindings
  • http://doi.acm.org/10.1145/1463434.1463497
    Findings
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