This document reviews spatiotemporal database models and query languages for moving objects. It discusses the increasing availability of mobility data and limitations of existing systems in handling dynamic geospatial data. Key points covered include spatiotemporal concepts like modeling space and time; data models representing moving objects and their trajectories; query languages extending SQL to handle spatial and temporal aspects; and open issues regarding dynamic attributes, uncertainty, and modeling objects in 3D/indoor spaces.

1. Spatiotemporal
Database Models and
Languages For
Moving Objects
A Review

2. Content
 Motivation
 Spatiotemporal Concepts
 Spatiotemporal Data Models
 Spatiotemporal Query Languages
 Open Issues
2010-06-17CISTI.2010@jps
2

3. Motivation
 Increasing availability of mobility data
 Existing applications of mobile data
 vehicle trajectories optimization
 leisure purposes
 location-based systems, …
 New applications are expected to emerge
 to find mobility patterns of people
 to track animals movements
 or any kind of moving objects (MO)
2010-06-17
3
CISTI.2010@jps

4. Motivation [2]
 Current Geographic Information Systems (GIS)
 conceived to process traditional, static or slow
changing, geospatial data
 are not suitable to support the MO dynamism
 The Database Management Systems (DBMS)
market leaders support spatial data applications
 However, modern relational DBMS:
 are not designed to run spatiotemporal queries
 context or semantic issues cannot be considered
in the storage process
2010-06-17
4
CISTI.2010@jps

5. Content
 Motivation
 Spatiotemporal Concepts
 Spatiotemporal Data Models
 Spatiotemporal Query Languages
 Open Issues
2010-06-17CISTI.2010@jps
5

6. Spatiotemporal Concepts
 Space: framework to formalize specific relationships
among a set of objects
 Spatial data refers to the
 position of objects and the
 space occupied by them
 Spatiotemporal: spatial data + time dimension.
 Most research about spatiotemporal data concerns
2D + T:
 2 space dimensions (2D)
 time dimension
2010-06-17CISTI.2010@jps
6

7. Conceptual models of space
 Set-based space
 relationships: element/set equality, subset, union,
etc.
 Topological space
 relationships: boundary, interior, open, closed,
within, connected, and overlaps
 Network space
 relationships: connectivity among nodes
 Euclidean space
 transforms spatial properties and relationships in
coordinates
2010-06-17CISTI.2010@jps
7

8. Modelling approach
 Spatial data models
 continuous: abstract model
 discrete: suitable for relational DBMS
 Field-based discrete data models
 spatial data: collection of spatial functions
 Transform space-partition (e.g. raster) to attribute
domain (height, rainfall, temp., etc.)
 Object-oriented discrete data models
 spatial data: collection of discrete, identifiable,
spatially referenced entities
 The objects are independent of their location
2010-06-17CISTI.2010@jps
8

9. Spatial data types
 4 basic abstract data types (Güting et al. , 2000)
 a point is a point in the Euclidean plan
 a points value is a finite set of points
 a line is a finite set of continuous curves
 a region is a finite set of disjoint parts/faces
 Discrete data models
 Implemented in current GIS as field-based (raster)
or object-based representations (vector).
 Basic data types: point, line and polygon
 To define the polygon, Worboys et al. (1990)
added: node, chain and ring.
2010-06-17CISTI.2010@jps
9

10. Spatiotemporal data types
 To capture time, Güting et al. (2000) defined two
other basic abstract data types:
 mpoint = time  point
 mregion = time  region
 and a closed system of operations was defined
 Based in the fact that abstract models are impossible
to implement, Forlizzi et al. (2000) proposed the
discrete data types:
 ureal
 upoint, upoints
 Uline,
 uregion
2010-06-17CISTI.2010@jps
10

11. Moving Objects (MO)
 Pervasive object that changes position or extend
continuously (Güting et al., 2000):
 trajectory = moving(point)  line
 The trajectory of a MO
 the data refers to the past, but
 can be useful to get current position and
 predict future movements
2010-06-17CISTI.2010@jps
11
(Praing & Schneider, 2007)

12. Dynamic attributes
 Prasad Sistla et al. (1997) classified attributes of
object-class databases as being static or dynamic
 The static attribute (common database attribute)
 needs explicit update to change its value
 The dynamic attribute
 changes continuously as a function of time
 does not require to be explicitly updated
 defined by three sub-attributes:
 the value
 the update time
 a time function
2010-06-17CISTI.2010@jps
12

13. Content
 Motivation
 Spatiotemporal Concepts
 Spatiotemporal Data Models
 Spatiotemporal Query Languages
 Open Issues
2010-06-17CISTI.2010@jps
13

14. Spatiotemporal data models
 Generic spatiotemporal data models were proposed
since early 1990’s
 Worboys et al. (1990) proposed an OO design
methodology to design GIS
 Shekhar et al. (1997) proposed a GIS Entity
Relational model (GISER)
 continuous fields are associated with
discretisation and interpolation models.
2010-06-17CISTI.2010@jps
14

15. Spatiotemporal data models for MO
 MO means continuously changing data
 MO position and extend could change quickly
 Requires high data update frequency, which could
cause performance problems to DBMS
 MO database should store predicted data and
provide query capability for querying such data
2010-06-17CISTI.2010@jps
15
(Praing and Schneider, 2007)

16. Spatiotemporal data models for MO [2]
 Sistla et al. (1997) proposed the Moving Objects
SpatioTemporal (MOST) data model
 designed to handle dynamic attributes
 to reduce the update frequency
 the result of a query will change on time, even if
the database is not updated
 The project Databases fOr MovIng Objects tracking
(DOMINO) had 4 requirements (Wolfson et al.,
1999):
 location modelling of MO
 query language for spatiotemporal data
 index of continuously changing data
 handle the uncertainly of MO query results.
2010-06-17CISTI.2010@jps
16

17. Spatiotemporal data models for MO [2]
 Praing and Schneider (2007) proposed the Future
Movements of Moving Objects (FuMMO) abstract
model:
 to define the future movement of MO, such as
points, lines or regions
 considering future evolutionary properties, such as
uncertainty and dimensional restrictions.
2010-06-17CISTI.2010@jps
17

18. Content
 Motivation
 Spatiotemporal Concepts
 Spatiotemporal Data Models
 Spatiotemporal Query Languages
 Open Issues
2010-06-17CISTI.2010@jps
18

19. Spatiotemporal query languages
 Spatiotemporal queries are difficult to express using
a usual query language (e.g. SQL)
 Typical queries: MO position or trajectory
 Several extensions to SQL were proposed
 The Future Temporal Logic (FTL) language allows
querying future states of the modelled system
 designed to be executed on the top of native
DBMS query language (DOMINO Project)
 queries are based on two basic future temporal
operators: until and nexttime
2010-06-17CISTI.2010@jps
19

20. Content
 Motivation
 Spatiotemporal Concepts
 Spatiotemporal Data Models
 Spatiotemporal Query Languages
 Open Issues
2010-06-17CISTI.2010@jps
20

21. Open Issues
 Dynamic attributes are not yet implemented in
existing data models and query languages:
 track real-time MO position
 predict future movement of objects
 Uncertainly constraints should be taken in account by
data models and languages
 Context or semantic issues can cause performance
problems to query current DBMS
 Need to extend the data models & languages:
 4 dimensional applications (3D space + time)
 indoor environments (space constraints)
2010-06-17CISTI.2010@jps
21

22. 2010-06-17CISTI.2010@jps 22
Spatiotemporal Database
Models and Languages For
Moving Objects
A Review
Thank You
Joaquim P. Silva
School of Technology
IPCA, Barcelos, Portugal
jpsilva@ipca.pt