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

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Building an ITEM for Data Mining (Bogorny 2009)Formats/types for an item:ameOnly: is the name of the stop/moveSTOPS: name of the spatial feature instance• IbisHotelMOVES: name of the two stops which define the move• SydneyAirport – IbisHotelameStart: is the name of the stop/move + start timeIbisHotel [morning]--stopLouvreMuseum [weekend]--stopIbisHotel-SydneyAirport [10:00AM-11:00AM] --move10/11/2010 GIScience 2010 – A conceptual data model for trajectory data miningVania Bogorny, Universidade Federal de Santa Catarina, Brazil, www.inf.ufsc.br/~vania79Building an ITEM for Data Mining (Bogorny 2009)ameEnd: name of a stop/move + end timeIbisHotel[morning]stopIbisHotel-SydneyAirport[10:00AM-11:00AM] moveameStartEnd: name of a stop/move + start time + end timeIbisHotel[08:00AM-11:00AM][1:00pm-6:00pm] stopLouvreMuseum[morning][afternoon] stopSydenyAirport– IbisHotel [10:00AM-11:00PM] [10:00AM-6:00PM]10/11/2010 GIScience 2010 – A conceptual data model for trajectory data miningVania Bogorny, Universidade Federal de Santa Catarina, Brazil, www.inf.ufsc.br/~vania80
Multiple-Granularity Semantic Trajectory DMQL (Bogorny 2009)ST-DMQL is an approach to semantically enrichtrajectories with domain informationAutormatically tranforms these semantic information intodifferent space and time granularitiesExtracts frequent patterns, association rules andsequential patterns from semantic trajectoriesMultiple Level Semantic Sequential PatternsLarge Sequences of Length 2 (ITEM=SPACE+Start_Time)(41803_street_5, 41803_street_5) Support: 7(41803_street_4, 41803_street_4) Support: 9(41803_street_4, 66655_street_4) Support: 5(41803_street_2, 41803_street_2) Support: 6(41803_street_8, 41803_street_8) Support: 5(41803_street_3, 0_unknown_3) Support: 5gidtime unit = monthSpatial feature type (stop name)
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: STOPS at Multiple-Granularities (Bo
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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