Tools for semantic trajectory data mining A importncia

Tools for semantic trajectory data mining

A importância de considerar a semântica C R R SC T 3 T 2 T 1 T 4 Padrão Geométrico C H T 1 H Hotel H T 4 R Restaurant C Cinema Padrão SEM NTICO (a) Hotel p/ Restaurante, passando por SC (b) Cinema, passando por SC

Multiple-granularity semantic trajectory pattern mining 9/15/2020 3 of 90

STOPS at Multiple-Granularities (Bogorny 2009) Stop at Ibis Hotel from 6: 04 PM to 7: 42 PM, september 16, 2010 space time Ibis. Hotel or Accommodation Afternoon or Thursday or 6: 00 PM – 8: 00 PM or RUSH-HOUR 9/15/2020 4 of 90

- the building blocks for semantic pattern discovery n An item is generated either from a stop or a move n An item is a set of complex information (space + time), that can be defined in many formats/types and at different granularities 9/15/2020 5 of 90

Building an ITEM for Data Mining (Bogorny 2009) n n Formats/types for an item: Name. Only: is the name of the stop/move u STOPS: name of the spatial feature instance • Ibis. Hotel u MOVES: name of the two stops which define the move • Sydney. Airport – Ibis. Hotel n Name. Start: is the name of the stop/move + start time u Ibis. Hotel [morning] --stop u Louvre. Museum [weekend] --stop u Ibis. Hotel-Sydney. Airport [10: 00 AM-11: 00 AM] --move 9/15/2020 6 of 90

Building an ITEM for Data Mining (Bogorny 2009) n n Name. End: name of a stop/move + end time u Ibis. Hotel[morning] stop u Ibis. Hotel-Sydney. Airport[10: 00 AM-11: 00 AM] move Name. Start. End: name of a stop/move + start time + end time u Ibis. Hotel[08: 00 AM-11: 00 AM][1: 00 pm-6: 00 pm] stop u Louvre. Museum[morning][afternoon] stop u Sydeny. Airport– Ibis. Hotel [10: 00 AM-11: 00 PM] [10: 00 AM 6: 00 PM]

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

Sequential Pattern Mining

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)

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

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

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

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

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

Sequential Patterns (Transportation Application) 16

Sequential Patterns (Transportation Application) 17

Sequential Patterns (Transportation Application) 18

Stops (Recreation Application) 19

Sequential Patterns (Recreation Application) 20

Ferramentas para Mineracao de Trajetorias

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

Weka-STDPM 23

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Weka-STDPM 25

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Analise de Comportamento do Objeto Movel

Avoidance

Chasing

Comportamento Anomalo