A short review about spatiotemporal Database Models and Languages For Moving Objects

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

2. Content
 Motivation
 Spatiotemporal Concepts
 Spatiotemporal Data Models
 Spatiotemporal Query Languages
 Open Issues
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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)
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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
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5. Content
 Motivation
 Spatiotemporal Concepts
 Spatiotemporal Data Models
 Spatiotemporal Query Languages
 Open Issues
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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
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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
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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
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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.
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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
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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
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(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
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13. Content
 Motivation
 Spatiotemporal Concepts
 Spatiotemporal Data Models
 Spatiotemporal Query Languages
 Open Issues
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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.
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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
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(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.
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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.
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18. Content
 Motivation
 Spatiotemporal Concepts
 Spatiotemporal Data Models
 Spatiotemporal Query Languages
 Open Issues
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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
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20. Content
 Motivation
 Spatiotemporal Concepts
 Spatiotemporal Data Models
 Spatiotemporal Query Languages
 Open Issues
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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)
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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