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Tools for semantic trajectory data mining

Tools for semantic trajectory data mining. C. R. C. R. H. H. H. R. C. Hotel. Restaurant. Cinema. Padrão SEMÂNTICO Hotel p/ Restaurante, passando por SC (b) Cinema, passando por SC. A importância de considerar a semântica. SC. T3. T3. T2. T2. T1. T1. T4. T4.

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Tools for semantic trajectory data mining

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  1. Tools for semantic trajectory data mining

  2. C R C R H H H R C Hotel Restaurant Cinema • Padrão SEMÂNTICO • Hotel p/ Restaurante, passando por SC • (b) Cinema, passando por SC A importância de considerar a semântica SC T3 T3 T2 T2 T1 T1 T4 T4 Padrão Geométrico

  3. Multiple-granularity semantic trajectory pattern mining

  4. STOPS at Multiple-Granularities (Bogorny 2009) Stop at Ibis Hotel from 6:04PM to 7:42PM, september 16, 2010 time space IbisHotel or Hotel or Accommodation Afternoon or Thursday or 6:00PM – 8:00PM or RUSH-HOUR

  5. - the building blocks for semantic pattern discovery An item is generated either from a stop or a move An item is a set of complex information (space + time), that can be defined in many formats/types and at different granularities

  6. Building an ITEM for Data Mining (Bogorny 2009) Formats/types for an item: NameOnly: is the name of the stop/move STOPS: name of the spatial feature instance IbisHotel MOVES: name of the two stops which define the move SydneyAirport – IbisHotel NameStart: is the name of the stop/move + start time IbisHotel [morning] --stop LouvreMuseum [weekend] --stop IbisHotel-SydneyAirport [10:00AM-11:00AM] --move

  7. Building an ITEM for Data Mining (Bogorny 2009) NameEnd: name of a stop/move + end time IbisHotel[morning] stop IbisHotel-SydneyAirport[10:00AM-11:00AM] move NameStartEnd: name of a stop/move + start time + end time IbisHotel[08:00AM-11:00AM][1:00pm-6:00pm]  stop LouvreMuseum[morning][afternoon] stop SydenyAirport– IbisHotel [10:00AM-11:00PM] [10:00AM-6:00PM]

  8. Multiple-Granularity Semantic Trajectory DMQL (Bogorny 2009) ST-DMQL is an approach to semantically enrich trajectories with domain information Autormaticallytranforms these semantic information into different space and time granularities Extracts frequent patterns, association rules and sequential patterns from semantic trajectories

  9. Sequential Pattern Mining

  10. Multiple Level Semantic Sequential Patterns Large 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: 5 time unit = month gid Spatial feature type (stop name)

  11. Large Sequences of Length 2 (ITEM=SPACE+Start_Time) (41803_street_tuesday,41803_street_tuesday) Support: 9 (41803_street_tuesday,66655_street_tuesday) Support: 5 (41803_street_monday,66655_street_monday) Support: 5 (41803_street_monday,41803_street_monday) Support: 11 (41803_street_monday,0_unknown_monday) Support: 5 (41803_street_thursday,41803_street_thursday) Support: 13 (41803_street_thursday,0_unknown_thursday) Support: 6 (41803_street_wednesday,41803_street_wednesday) Support: 7 Multiple Level Semantic Sequential Patterns Time unit = Day of the week gid Spatial feature type (stop name)

  12. Resultados obtidos com os Métodos que Agregam Semântica - Trajetórias de Carros

  13. item=name(instance) + start Time(month) • Large Sequences of Length 2 • (41803_ruas_5,41803_ruas_5) Support: 7 • (41803_ruas_4,41803_ruas_4) Support: 9 • (41803_ruas_4,66655_ruas_4) Support: 5 • (41803_ruas_2,41803_ruas_2) Support: 6 • (41803_ruas_8,41803_ruas_8) Support: 5 • (41803_ruas_3,0_unknown_3) Support: 5 month gid Spatial feature type

  14. item=name(instance) + startTime(weekday/weekend) • Large Sequences of Length 3 • (41803_ruas_weekday,41803_ruas_weekday,66655_ruas_weekday) Support: 6 • (41803_ruas_weekday,66640_ruas_weekday,66655_ruas_weekday) Support: 7 • Large Sequences of Length 2 • (0_unknown_weekday,41803_ruas_weekday) Support: 5 • (41803_ruas_weekday,0_unknown_weekday) Support: 16 • (41803_ruas_weekday,66658_ruas_weekday) Support: 8 • Large Sequences of Length 1 • (66584_ruas_weekday) Support: 10

  15. item=name(instance) + start time = day of the week • Large Sequences of Length 2 • (41803_ruas_tuesday,41803_ruas_tuesday) Support: 9 • (41803_ruas_tuesday,66655_ruas_tuesday) Support: 5 • (41803_ruas_monday,66655_ruas_monday) Support: 5 • (41803_ruas_monday,41803_ruas_monday) Support: 11 • (41803_ruas_monday,0_unknown_monday) Support: 5 • (41803_ruas_thursday,41803_ruas_thursday) Support: 13 • (41803_ruas_thursday,0_unknown_thursday) Support: 6 • (41803_ruas_wednesday,41803_ruas_wednesday) Support: 7

  16. Sequential Patterns (Transportation Application)

  17. Sequential Patterns (Transportation Application)

  18. Sequential Patterns (Transportation Application)

  19. Stops (Recreation Application)

  20. Sequential Patterns (Recreation Application)

  21. Ferramentas para Mineracao de Trajetorias

  22. Weka-STDPM • Ferramentacriadaporalunos da UFRGS e UFSC • Extensao da FerramentaWeka, criadana Nova Zelandiapara • Mineracao de dados

  23. Weka-STDPM

  24. Weka-STDPM

  25. Analise de Comportamento do ObjetoMovel

  26. Avoidance

  27. Chasing

  28. ComportamentoAnomalo

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