Tools for semantic trajectory data mining. 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.

Slides:

Advertisements

Similar presentations

Hierarchical Floorplanning of Chip Multiprocessors using Subgraph Discovery Javier de San Pedro Jordi Cortadella Antoni Roca Universitat Politècnica de.

Advertisements

Semantic-based Trajectory Data Mining Methods

Rule Discovery from Time Series Presented by: Murali K. Kadimi.

Zhou Zhao, Da Yan and Wilfred Ng

The Semantic Web-Week 22 Information Extraction and Integration (continued) Module Website: Practical this week:

Time 1 day = 24 hours 1 hour = 60 minutes 1 minute = 60 seconds 1 year

Events  Art Beat on Mean Street- May 30, :30pm- 9:30pm always last Saturday of May  Art in the park on flag day- June 14, :00am-12:00pm.

Spatio-Temporal Data Mining Vania Bogorny Universidade Federal do Rio Grande do Sul Shashi Shekhar University.

2.3 Ruler problems and number lines Objective: To solve ruler problems.

Calendar Questions Number Sense Fun with Patterns Time and Money

A Paradigm for Space Science Informatics Kirk D. Borne George Mason University and QSS Group Inc., NASA-Goddard or

 Quiz #3 Oct. 27th  Writing Assignment Due Oct. 27 th  Homework 7 due Nov. 3rd  Exam #3 Thursday Nov. 3rd.

WindMine: Fast and Effective Mining of Web-click Sequences SDM 2011Y. Sakurai et al.1 Yasushi Sakurai (NTT) Lei Li (Carnegie Mellon Univ.) Yasuko Matsubara.

CENTRE Cellular Network’s Positioning Data Generator Fosca GiannottiKDD-Lab Andrea MazzoniKKD-Lab Puntoni SimoneKDD-Lab Chiara RensoKDD-Lab.

Week 9 Data Mining System (Knowledge Data Discovery)

Temporal Data Mining Claudio Bettini, X.Sean Wang and Sushil Jajodia Presented by Zhuang Liu.

SeCoGIS - Bercellona, October 20, 2008 An ontology-based approach for the semantic modeling and reasoning on trajectories Miriam Baglioni Jose de Macedo.

FEP 114 – Aula 3b. Objetivo: Determinar o intervalo de temperatura em que os calorímetros utilizados podem ser considerados ideais. Determinar o calor.

FEP 114 – Pêndulo de Torção Aula 2 Pêndulo de Torção.

Measuring Time.

RECURSIVE PATTERNS WRITE A START VALUE… THEN WRITE THE PATTERN USING THE WORDS NOW AND NEXT: NEXT = NOW _________.

Evaluating Remote Sensing Data Or How to Avoid Making Great Discoveries by Misinterpreting Data Richard Kleidman ARSET-AQ Applied Remote Sensing Education.

The Inductive Software Engineering Manifesto Principles for Industrial Data Mining Paper Authored By: Menzies & Kocaganeli – Lane Dept of CS/EE, WVU Bird,

GeoPKDD Geographic Privacy-aware Knowledge Discovery and Delivery Kick-off meeting Pisa, March 14, 2005.

Get Ready to Tour (insert country name) (Your name)

Presentation 3: Cube and DW Implementation Adwait Mulye Yuga Pawar Floyd J. Srubar Vidyasagar Velamala.

 Larger datasets are becoming available from GPS, GSM, RFID, and other sensors.  Interest in movement has shifted from raw movement data analysis to.

Mining Multidimensional Sequential Patterns over Data Streams Chedy Raїssi and Marc Plantevit DaWak_2008.

Data Mining Association Rules: Advanced Concepts and Algorithms

Good Morning Las Vegas!. Travis has 7 pet mice. He keeps them in two cages that are connected so that the mice can go back and forth between the.

© J. Christopher Beck Lecture 24: Workforce Scheduling.

Semantic Aspects in Spatial Data Mining Vania Bogorny.

THE FUTURE: VERBAL TENSES FUTURE TENSES - Will / - Going to PRESENT TENSES - Present continuous / Present simple.

Christoph F. Eick Questions and Topics Review November 11, Discussion of Midterm Exam 2.Assume an association rule if smoke then cancer has a confidence.

S calable K nowledge C omposition Ontology Interoperation January 19, 1999 Jan Jannink, Prasenjit Mitra, Srinivasan Pichai, Danladi Verheijen, Gio Wiederhold.

Language-Independent Discriminative Parsing of Temporal Expressions CS 671 : Natural Language Processing - Gabor Angeli, Jakob Uszkoreit.

About the Festival Sally Ride Science Festivals are designed to encourage 5th-8th grade girls in science. An inspiring talk given by astronaut Ellen Ochoa.

Time Span Analysis David Attaway.

S EQUENTIAL P ATTERNS & THE GSP A LGORITHM BY : J OE C ASABONA.

Towards Semantic Trajectory Outlier Detection Artur Ribeiro de Aquino 1 Luis Otavio Alvares 1 Chiara Renso 2 Vania Bogorny Dep. de Matemática e Estatística.

Intelligent DataBase System Lab, NCKU, Taiwan Josh Jia-Ching Ying 1, Wang-Chien Lee 2, Tz-Chiao Weng 1 and Vincent S. Tseng 1 1 Department of Computer.

Introduction to Data Mining by Yen-Hsien Lee Department of Information Management College of Management National Sun Yat-Sen University March 4, 2003.

Telling the Time.

Routine Phrases Sign in ASL order: Time, Object, Subject, Verb.

For the sequence, describe the pattern and write the next term. 1.) 1, 6, 11, 16 2.) -4, 8, -12, 16 3.) 1.2, 4.2, 9.2, 16.2.

RADFORD TRANSIT BY: VIRGINIA & ALEC. THE KNOW HOW: Is the transit free? Which line do you take? How often does the bus come? Does the bus only go to places.

1 Number Practice Money, Time, Age, etc. 2 New added Sign Mins - Time Month - Context Year - Length Hour (1 - 10) Hour (11 upward) Pence Twist - 1 st.

Information Extraction. Extracting Information from Text System : When would you like to meet Peter? User : Let’s see, if I can, I’d like to meet him.

Vocabulary for the month of March creado por señora Bennett.

Shape, space and measure posters months

Math Practice platform

Present continuous FUTURE ARRANGEMENTS.

Sequential Pattern Mining Using A Bitmap Representation

A Day in the life 12 am 9 pm 3 am Night Dinner and evening 6 pm 6 am

(TYPE HOTEL NAME HERE) HOUSEKEEPING HEROES International Housekeeping

Everyday Spanish Basic 6-week Spanish Language Courses

(TYPE HOTEL NAME HERE) HOUSEKEEPING HEROES International Housekeeping

Transportation: Day 2 For those that need to get to the airport early, there will a bus to the airport at 9:30am and a bus at 11:00am. For those attending.

Unit 8 Space travel Freeway - Vol. 2 GRAMMAR

Los usos de por y para Imagina Lección 4-3.

Twenty Questions Subject: Time.

A conceptual Data Model for Trajectory Data Mining

(TYPE HOTEL NAME HERE) HOUSEKEEPING HEROES International Housekeeping

(TYPE HOTEL NAME HERE) HOUSEKEEPING HEROES International Housekeeping

Visually Analyzing Latent Accessibility Clusters of Urban POIs

From: xxxxxxx Sent: Thursday, September 5, :34 AM To: xxxxxxxxx Subject: RE: RE: Classes “The issue has been that Alice [the teacher] primarily.

Comments Task AS1 Tasks 12 Given a collection of boolean spatial features, the co-location pattern discovery process finds the subsets of features that.

Presentation transcript:

Tools for semantic trajectory data mining

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

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

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

- 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

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

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]

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

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

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

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 gid Spatial feature type month

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)

Ferramentas para Mineracao de Trajetorias

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

23 Weka-STDPM

24

25 Weka-STDPM

26

27

28

Analise de Comportamento do Objeto Movel

Avoidance

Chasing

Comportamento Anomalo