Trajectory Pattern Mining

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Trajectory Pattern Mining

• Fosca Giannotti, Mirco Nanni,
• Dino Pedreschi, Fabio Pinelli
• KDD Lab (ISTI-CNR Univ. Pisa)
• Presented by Qiming Zou

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Overview

• Motivations
• Trajectory
• T-Pattern
• Regions of Interest
• Future Work
• QA

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Motivations

• Large number of mobile devices, mobile services
  available

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Motivations

• It is possible to collect position traces from
  such devices
• We can extract information and patterns from
  these data to describe mobility behaviors
• Use this information for fields such as urban
  planning

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Trajectory

• Trajectories are sequences that contain the
  spatial and temporal information about movements

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Trajectory

• Trajectories are usually given as spatiotemporal
  (ST) sequences
• lt(x0, y0, t0), ..., (xn, yn, tn)gt
• xi, yi is the position coordinate relative to the
  origin
• ti is the time stamp for the position information

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Trajectory

• 2D and 3D representation of a trajectory

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T-Pattern

• A Trajectory Pattern (T-Pattern) is a couple (s,
  a), where
• s lt(x0, y0),..., (xn, yn)gt is a sequence of n1
  locations
• a lta1,..., angt are the transition times such
  that ai ?ti ti ti-1

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T-Pattern

• A T-Pattern Tp occurs in a trajectory if it
  contains a subsequence S such that
• each (xi, yi) in Tp matches a point (xi, yi) in
  S
• the transition times in Tp are similar to those
  in S

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T-Pattern

• The same exact spatial location (x, y) usually
  never occurs
• Yet, close locations often represent the same
  place
• The same exact transition times usually do not
  occur often
• However, close times often indicate similar
  behavior

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T-Pattern

• To solve the problem, we introduce the notions
  of
• Spatial neighborhood Two points match if one
  falls within a spatial neighborhood N() of the
  other
• Temporal tolerance Two transition times match if
  their temporal difference is t

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T-Pattern

• Example

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Regions of Interest

• It is too computational intensive and yield
  little practical use to generate all T-Patterns
• Solution Use a Regions of Interest approach,
  only use these regions as nodes of the T-Patterns

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Regions of Interest

• Given a set of Regions of Interest R, define the
  neighborhood of (x, y) as
• Neighbors belong to the same region
• Points in no region have no neighbors

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Regions of Interest

• Slt(x0, y1, t1), ..., (x4, y4, t4)gt
• gt
• lt(R4, t0), (R3, t2), (R3, t3), (R1, t4)gt

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Regions of Interest

• What if the Regions of Interests are not known
  before hand?
• Define heuristics for automatic Regions of
  Interest extraction from data
• Geography-based (crossroads)
• Usage-based (popular places)
• Mixed (popular squares)

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Future Work

• Application-oriented tests on large, real
  datasets
• Study relations with
• Geographic background knowledge
• Privacy issues
• Reasoning on trajectories and patterns

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Trajectory Pattern Mining

• Questions?