1. A conceptual Data Model for Trajectory Data Mining
Universidade Federal de Santa Catarina, Florianopolis, Brazil
Informatics and Statistics Department
A conceptual Data Model for Trajectory Data Mining
* Prof. Vania Bogorny (INE/UFSC - Brazil)
Prof. Carlos Alberto Heuser (II/UFRGS - Brazil)
Prof. Luis Otavio Alvares (II/UFRGS-Brazil)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

2. GIScience 2010 – A conceptual data model for trajectory data mining
Outline
Motivation
Objective
Basic concepts
Proposed Model
Evaluation
Conclusion
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

3. Introduction and Motivation
On the one side (database technology )
Since its origin, database design has the purpose of modeling data for operational purposes only
Database designers don't think about data mining during the conceptual database design
Data mining (DM) and knowledge discovery (KDD) from databases has become very popular in the last years in many fields and several application domains
Dozens of papers with new data mining algorithms have been published in the last decade, but very little has been done for the automatic data preprocessing for (DM), which is the most time consuming step. Statistically it takes between 60 and 80% of the whole discovery process
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 3 3

4. Introduction and Motivation
On the other side (artificial intelligence )
Data mining (DM) or knowledge discovery (KDD) from databases has become very popular in the last years in many fields and several application domains
Dozens of new data mining algorithms have been proposed in the last decade,
but very little has been done for the automatic data preprocessing, which is the most time consuming step
Data mining (DM) and knowledge discovery (KDD) from databases has become very popular in the last years in many fields and several application domains
Dozens of papers with new data mining algorithms have been published in the last decade, but very little has been done for the automatic data preprocessing for (DM), which is the most time consuming step. Statistically it takes between 60 and 80% of the whole discovery process
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 4 4

5. Introduction and Motivation
DATABASE Modelling
(Normalization)
DATA MINING
(Disnormalization)
One single file
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

6. Introduction and Motivation
Another problem for data mining:
data have to be preprocessed and transformed into different granularities
Examples:
Louvre Museum  Museum  TuristicPlace
Instance + type
type
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

7. Introduction and Motivation
These problems increase when dealing with trajectories of moving objects, which is the focus of this paper
This is specially true for trajectories of moving objects, which is a new kind of spatio-temporal data generated by mobile devices, that in the last few years have become very popular in daily life.
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

8. GIScience 2010 – A conceptual data model for trajectory data mining
Objective
We propose a conceptual framework for trajectory database modeling that supports data mining
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

9. GIScience 2010 – A conceptual data model for trajectory data mining
Basic Concepts
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 9

10. GIScience 2010 – A conceptual data model for trajectory data mining
Trajectories are new kind of spatio-temporal data
Trajectories have attracted intensive research in both databases and data mining communities
This is specially true for trajectories of moving objects, which is a new kind of spatio-temporal data generated by mobile devices, that in the last few years have become very popular in daily life.
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

11. GIScience 2010 – A conceptual data model for trajectory data mining
Trajectory Raw Data
Trajectory Data are:
Spatio-temporal data
Represented by a set of points located in space and time
Form: (tid, x,y,t), where tid is the trajectory identifier, (x,y) represent the spatial location at time t
Tid position (x,y) time (t)
:25
:26
:40
:41
:42
:04
:05
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

12. The Model of Stops and Moves (Spaccapietra 2008)
Important parts of trajectories
Where the moving object has stayed for a minimal amount of time
Stops are application dependent
Tourism application
Hotels, touristic places, airport, …
Traffic Management Application
Traffic lights, roundabouts, big events…
MOVES
Are the parts that are not stops
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

13. Semantic Trajectories
A semantic trajectory is a set of stops and moves
Stops have by a place, a start time and an end time
Moves are characterized by two consecutive stops
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

14. STOPS at Multiple-Granularities
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
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

15. ITEMS - 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
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

16. Building an ITEM for Data Mining
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
ZurichAirport – IbisHotel
NameStart: is the name of the stop/move + start time
IbisHotel [morning] stop
LouvreMuseum [weekend] stop
IbisHotel-ZurichAirport [10:00AM-11:00AM] --move
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

17. Building an ITEM for Data Mining
NameEnd: name of a stop/move + end time
IbisHotel[morning] stop
IbisHotel-ZurichAirport[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
ZurichAirport– IbisHotel [10:00AM-11:00PM] [10:00AM-6:00PM]
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

18. Semantic Trajectory Patterns
Frequent Patterns
Sequential Patterns and Association Rules
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 18

19. Trajectory Frequent Pattern
Is a set of items that occur a minimal number of times (support s)
Examples:
{LouvreMuseum [08:00-10:00]} (s=0.1)
{Airport [morning], hotel [morning]} (s=0.2)
{Airport-Hotel, Hotel-Museum} (s=0.15)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

20. Trajectory Sequential Pattern
Is an ordered list of items that occur a minimal number of times (support s)
Examples:
<Airport[morning], Hotel[morning], Museum[afternoon] > (s=0.15)
<Airport-Hotel, Hotel-Museum> (s= 0.1)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

21. Trajectory Association Rule
Is a rule where the items occur a minimal number of times (support s) and with a minimal confidence (c)
Example
Airport[morning], Hotel[morning]  Museum[afternoon] (s=0.1) (c=0.5)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

22. GIScience 2010 – A conceptual data model for trajectory data mining
The Proposed Model
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 22

23. GIScience 2010 – A conceptual data model for trajectory data mining
The Proposed Model
We extend the model of stops and Moves proposed by Spaccapietra with new attributes and methods
Add new classes and relationships, with attributes and methods to automatic data preprocessing and multiple-level mining
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

24. The Conceptual Data Model of Stops and Moves (Spaccapietra 2008)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

25. GIScience 2010 – A conceptual data model for trajectory data mining
Proposed OO Model
Compute and
Store the patterns
Data
Pre-processing
The data to be mined will be stops or moves. Therefore, we build the item over stops and moves
ITEM has the objective to aggregate and disaggregate data for mining
Time in stop and move is a timestamp (specific time), while in BESITEM and MOVEITEM, time is na aggregate time
Spaccapietra´s
Model
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 25 25

26. GIScience 2010 – A conceptual data model for trajectory data mining
Proposed OO Model
Stops and Moves are extended with new attributes (specific time, e.g. 07:10 – 08:05 ) and methods to instatiate stops and moves
The data to be mined will be stops or moves. Therefore, we build the item over stops and moves
ITEM has the objective to aggregate and disaggregate data for mining
Time in stop and move is a timestamp (specific time), while in BESITEM and MOVEITEM, time is na aggregate time
Concept Hierarchy for the spatial feature type (e.g.: AccomodationPlace  Hotel)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
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27. Proposed Model OO Model
Generic class to represent the 3 kinds of patterns
Attributes: support, listOfItems
Methods: countSupport(), sequentialPattern()
Attributes: support, confidence, antecedent (set of items) and consequent (set of items)
Methods: countSupport(), associatePattern(), and computeConfidence()
Attributes : startT, endT (generic time, e.g. Morning)
Methods:
getGenericSpatialFeature() – retrieves the hierarchy level
timeG() – generalizes time
spaceG() – generalizes space based on the hierarchy
buildItem() – creates generalized ITEM
Frequent Patterns:
Attributes: support, setOfItems
Methods: countSupport(), frequentPattern()
The data to be mined will be stops or moves. Therefore, we build the item over stops and moves
ITEM has the objective to aggregate and disaggregate data for mining
Time in stop and move is a timestamp (specific time), while in BESITEM and MOVEITEM, time is na aggregate time
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 27 27

28. Example of an Instantiated Model
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 28

29. Schema of Stops and Moves
STOP (Tid integer, Sid integer, SFTname string,
SFid integer, startT timestamp, endT timestamp)
Ex.: stop (1,1,Hotel, 3, 10AM, 11AM)
MOVE (Tid integer, Mid integer, SFT1name string,
SF1id integer, SFT2name string, SF2id integer,
startT timestamp, endT timestamp,
the_move geometry)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

30. GIScience 2010 – A conceptual data model for trajectory data mining
Schema of the Patterns
FrequentPattern/
SequentialPattern (Pid integer, pattern itemSetType, support real)
itemSetType (SFT1name string, SF1id integer, SFT2name string,
SF2id integer, startT string, endT string)
AssociatePattern (Pid integer, antecedent itemSetType,
consequent itemSetType, support real,
confidence real)
Nested relation
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

31. Instantiating and Querying Patterns
To instantiate the patterns we can use the ST-DMQL proposed in (Bogorny 2009)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 31

32. Instantiating Stops and Moves
SELECT generateS (method, candidateStops, buffer)
FROM trajectory
SELECT generateM (method, candidateStops, buffer)
IB-SMOT
CB-SMOT
DB-SMOT
......
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 32

33. Instatiating Sequential Patterns
Q1 (tourism application): Which are the sequences of moves that occur most frequently in the morning and in the evening? SELECT sequentialPattern (itemType = NameEnd, timeG = [8:00-12:00 AS morning, 18:00-23:00 AS evening], spaceG = instance, minsup=0.03) FROM move
Method in the ST-DMQL
Ans:
{IbisHotel - NotreDame[morning], EiffelTower – IbisHotel [evening]} (s=0.04)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

34. Example of Pattern Queries
Q: How many moves of sequential patterns cross Pont Neuf bridge?
SELECT count(m.*)
FROM sequentialPattern s, bridge b, move m
WHERE s.pattern.SFT1name=m.SFT1name AND s.pattern.SF1id=m.SF1id AND
s.pattern.SFT2name=m.SFT2name AND s.pattern.SF2id=m.SF2id AND
b.name='Pont Neuf' AND intersects (m.the_geom,b.the_geom)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 34

35. GIScience 2010 – A conceptual data model for trajectory data mining
Conclusions
Data pre-processing is the most time consuming step for DM and KDD
To think about data mining during the conceptual design of a database can significantly reduce these steps
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 35

36. GIScience 2010 – A conceptual data model for trajectory data mining
Conclusions
The proposed model:
Reduces the pre-processing tasks
Supports mining at multiple granularity levels
Automatically prepares the data for data mining
* Stores the patterns for futures queries
Multiple-granularities
data
patterns
Queries
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 36

37. Geometric Patterns X Semantic Patterns (Bogorny 2009)TP
Touristic
Place R
Restaurant CC CC T3 H
Hotel T2 T3 T2 T1 T4 T1 T4
Semantic trajectory Pattern
Hotel to Restaurant, passing by CC
(b) go to Cinema, passing by CC
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 37

38. GIScience 2010 – A conceptual data model for trajectory data mining
Thank You!
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 38

39. More examples for generating stops
SELECT generateS (CB-SMOT, [Hotel,60,TouristicPlace,15,ShoppingCenter,30], 5)
FROM trajectory t, district d
WHERE d.name='Bela Vista' and intersects (t.movingpoint.geometry, d.geometry)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil, 39

40. GIScience 2010 – A conceptual data model for trajectory data mining
Querying Rules
Suppose that the user is interested in association patterns which have weekend as the time dimension in the antecedent of the rule SELECT * FROM associatePattern WHERE antecedent.startT='weekend' or antecedent.endT='weekend'
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

41. Basic Concepts: Support
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

42. Basic Concepts: Semantic Trajectory Patterns
Example
Sequential Patterns: Let T be a set of semantic trajectories. A sequence of items X={x1; x2; ... ; xn}, ordered in time, is a trajectory sequential pattern with respect to T and minSup if s(X) >= minSup.
Example:
Work [morning], ShoppingCenter [afternoon], Gym [afternoon] (s=0.08%)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

43. Basic Concepts: Semantic Trajectory Patterns
Example
Home [night], Work [afternoon]  Gym [afternoon] (s=0.10%) (c=0.50)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

44. Basic Concepts: Semantic Trajectory Patterns
Example
ReligiousPlace [weekend], Restaurant [weekend] (s=0. 07)
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

45. GIScience 2010 – A conceptual data model for trajectory data mining
Related Works
Mining Trajectory Samples – Extract Geometric Patterns
Mining Semantic Trajectories or Trajectory pre-processing for mining
Attempts to reduce the gap between databases and data mining
Laube 2002, 2005
Giannotti 2007
Lee 2007
Cao 2006, 2007
Li 2010
Alvares 2007
Zhou 2007
Palma 2008
Bogorny 2009
Manso 2010
Data mining query languages, but not for trajectories
(Wang 2003,
Malerba 2004,
Han 1995) b
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,

46. Example of Frequent Pattern Instantiation
Q2: Which are the types of places most frequently visited by tourists on weekdays and weekends?
SELECT frequentPattern (itemType =NameStart, timeG = WEEKEND-WEEKDAY, spaceG = [type, GenericHotel = 1], minsup = 0.15)
FROM stop
Ans:
{4StarsHotel[weekend], Museum[weekend], Restaurant[weekend] } (s=0.16)
Method in the ST-DMQL
2/22/2019
GIScience 2010 – A conceptual data model for trajectory data mining
Vania Bogorny, Universidade Federal de Santa Catarina, Brazil,