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

More documents

Recommendations

Info

Athena (Baglioni 2009)1. ExampleStopSemanticTrajectoryMoveCommuterTrajectoryontologyCommuter trajectory≡ a trajectory frequentlystarting outside the city, stopping inside the city for a longtime and going back outside the cityAthena (Baglioni 2009)SELECT t.id, t.objectFROM Milano_trWHERE ‘Commuter’ inSEMANTIC(t.object)Given the trajectories, we query the system toidentify the ones whose type is commuter, i.e.satisfying the ontology definition
Pattern Interpretation (Rebeca 2010)This work focuses on postprocessing,trying to interpret thepatternsConsidering that the movementcontext is essential to correctlyinterpret and understand thepatternsCONTEXT = geography + thematicattributesPattern Interpretation (Rebeca 2010)1. Mining movement patterns (stops)2. Semantic enrichment: annotates patterns withinformation obtained from trajectory types defined inONTOLOGIES (e.g. the trajectory of a commuter)3. Use the enriched representation to automaticallyclassify patterns
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 and 12: Geometry-based Trajectory Data Mini
Page 13 and 14: Relative Motion Patterns (Laube 200
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: Athena (Baglioni 2009)Semantic-rich
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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?