Tutorial on Spatial and Spatio-Temporal Data Mining Part II ... - UFSC

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Relative Motion Patterns (Laube 2004)Proposed 5 kinks of trajectory patterns based on movement,direction, and location: convergence, encounter, flock,leadership, and recurrenceConvergence: At least m entities pass through the samecircular region of radius r, not necessarily at the sametime (e.g. people moving to train station)T 1T 2T 3T 4T 5convergenceRelative Motion Patterns (Laube 2004)Flock pattern: At least m entities are within a region of radius r and move inthe same direction during a time interval >= s (e.g. traffic jam)Leadership: At least m entities are within a circular region of radius r, theymove in the same direction, and at least one of the entities is heading in thatdirection for at least t time steps. (e.g. bird migration, traffic accident)Encounter: At least m entities will be concurrently inside the same circularregion of radius r, assuming they move with the same speed and direction.(e.g. traffic jam at some moment if cars keep moving in the same direction)T 2T 3LeadershipT 1EncounterFlock
Relative Motion Patterns (Laube 2004)Recurrence: at least m entities visit acircular region at least k timesF1F 1RecurrenceF 1F1Extension of the work proposed by [Laube 2004, 2005]Gudmundsson(2006)Computes the longest duration flock patternsThe longest pattern has the longest durationAnd has at least a minimal number oftrajectoriesGudmundsson (2007)proposes approximate algorithms for computingthe patterns leadership, encounter,convergence, and flockFocus relies on performance issues
Page 1 and 2: Tutorial on Spatia
Page 3 and 4: The Wireless A Explosão Explosion
Page 5 and 6: Mobility Data AnalysisAnimal Migrat
Page 7 and 8: Trajectories: Overall Characteristi
Page 9 and 10: Mining Trajectories : Frequent patt
Page 11: Geometry-based Trajectory Data Mini
Page 15 and 16: Co-Location Patterns (Cao 2006)Co-l
Page 17 and 18: T-Patterns (Giannotti, 2007)Sequent
Page 19 and 20: TraClass Algorithm (Lee 2008)Two ma
Page 21 and 22: TraClass Algorithm (Lee 2008)Second
Page 23 and 24: TraOD - Trajectory Outlier Detectio
Page 25 and 26: SummaryThese data mining approaches
Page 27 and 28: Semantic-based Spatio-temporal Data
Page 29 and 30: DJ-Cluster (Zhou 2007)DJ-Cluster is
Page 31 and 32: Semantic TrajectoriesA semantic tra
Page 33 and 34: IB-SMoT (Alvares 2007a)A stop of a
Page 35 and 36: CB-SMoT: Speed-based clustering (Pa
Page 37 and 38: Semantic Rich Trajectories (Transpo
Page 39 and 40: STOPS at Multiple-Granularities (Bo
Page 41 and 42: Multiple-Granularity Semantic Traje
Page 43 and 44: Weka-STDMWeka-STDM
Page 45 and 46: Athena (Baglioni 2009)Semantic-rich
Page 47 and 48: Pattern Interpretation (Rebeca 2010
Page 49 and 50: ReferencesManso, J. A. ; TIMES, V.
Page 51 and 52: Challenges and Open Issues in Spati

Tutorial on Spatial and Spatio-Temporal Data Mining Part II ... - UFSC

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