1. Tools for semantic trajectory data mining

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

3. Multiple-granularity semantic trajectory pattern mining 6/22/20153 of 90

4. Afternoon or Thursday or 6:00PM – 8:00PM or RUSH-HOUR IbisHotel or Hotel or Accommodation STOPS at Multiple-Granularities (Bogorny 2009) Stop at Ibis Hotel from 6:04PM to 7:42PM, september 16, 2010 space time 6/22/20154 of 90

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 6/22/20155 of 90

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 6/22/20156 of 90

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 Autormatically tranforms 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 gid Spatial feature type (stop name) time unit = month

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 gid Spatial feature type (stop name) Time unit = Day of the week Multiple Level Semantic Sequential Patterns

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

13. 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 gid Spatial feature type month

14. 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. 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. 16 Sequential Patterns (Transportation Application)

17. 17 Sequential Patterns (Transportation Application)

18. 18 Sequential Patterns (Transportation Application)

19. 19 Stops (Recreation Application)

20. 20 Sequential Patterns (Recreation Application)

21. Ferramentas para Mineracao de Trajetorias

22. 22 Weka-STDPM Ferramenta criada por alunos da UFRGS e UFSC Extensao da Ferramenta Weka, criada na Nova Zelandia para Mineracao de dados

23. 23 Weka-STDPM

24. 24

25. 25 Weka-STDPM

26. 26

27. 27

28. 28

29. Analise de Comportamento do Objeto Movel

30. Avoidance

31. Chasing

32. Comportamento Anomalo