Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 1
Data Enrichment for Adaptive Generalization
from a Multiresolution Database
Moritz Neun
SNF-Project DEGEN 4/2004 - 4/2007
Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 2
Context
DEGEN
=
Data Enrichment for the Control
of the Generalization Process
(Stefan Steiniger)
&
Data Enrichment for Adaptive Generalization
from a Multiresolution Database
(Moritz Neun)
Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 3
Summary
1. Introduction
2. Data Enrichment
• Defining Relations
• Classifying and Modeling Relations
• Extracting Relations
• Representing Relations
• Exploiting Relations
3. Time Table, Conferences & Publications
4. Conclusion
Slides english
Präsentation deutsch
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1. Introduction
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Generalization
Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005 6
Generalization
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Data Enrichment
... data enrichment is necessary to equip the ”raw” spatial data with additional information which can be used for a variety of purposes within the overall generalization process:
• characterization (priority, groups, relationships)• conflict detection• algorithm and parameter selection
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Multiresolution Databases (MRDB)
Multiresolution ≠ Multirepresentation• Different Levels of Detail (LOD)
are stored in one Database.• Common for web mapping
services (zooming)
Important for Generalization• Objects on different LODs
are linked
Database Technologies• Object Oriented (e.g. Gothic)• (Object) Relational (e.g. ArcSDE)
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Thematic Maps
Most research in generalization on topographic maps
majority of maps are of thematic nature (categorical, GIS, facilities, networks, POI ...)
focus on thematic mapswith polygons ina generic approach
Examples: geology, landuse, statistics, administration
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Research Purpose
The purpose of DEGEN is
data enrichment, the modeling of the enriched data
and the exploitation of this enriched data
for generalizing thematic maps
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Data Enrichment:
2.1 Defining Relations
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Definitions
Relations are a kind of property defined between two modifiable object types ...
A relation can be
one-to-one, one-to-many or many-to-many ...
Map Objects are the representation of a real world objects in the map data model. We distinguish simple and complex map objects (groupings). Each map object consists of its semantics (name, attributes, ...), its geometry and its topology.
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Horizontal & Vertical Relations
Horizontal relations of map objects exist within one specific scale or level of detail (LOD) and represent common structural properties.
Vertical relations are links between single map objects or groups of map objects between different map scales and LODs.
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Horizontal Relations
HorizontalRelations
Geometry Topology StructureStatistics &
DensitySemantics
Presented last semester by Stefan Steiniger
5 groups of measures for expressing horizontal relations
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Vertical Relations
• changes between single map objects• changes of properties for the whole LOD
link map objects across different LODs enrich the links with additional information about their
characteristics (properties)
VerticalRelations
map objectrelations
LODrelations
identity relation(micro object)
group relation(meso object)
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Using Relations
• Interpolation of intermediate scale levels
(Cecconi 2003) e.g. in combination with morphing
• Incremental updating of lower detailed LODs (Kilpeläinen
and Sarjakoski 1995)• choice of appropirate algorithms
• more information about parameters for algorithms
• better evaluation of results
• Training and use of learning algorithms (inductive, neuronal)
by analyzing relations and properties (Weibel et al. 1995)
• ...
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Working Hypothesis
The integration of enriched information into a MRDB allows the use of more sophisticated generalization algorithms, accelerates adaptive generalization, and helps to determine and maintain important structures across different scale levels.
This enriched information can be gained by analyzing, modeling and extracting relations between map objects.
Vertical Relations, being links between map objects on two different LODs, are representing abstract knowledge about the generalization from the higher to the lower map scale.
Classification
of Relations
Modeling
of Relations
Extraction
of Relations
Storage
of Relations
Exploitation
of Relations
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Research Questions
• What types of “vertical” relations between map objects on different levels of detail can be established?
• How can these relations effectively be modelled and represented in a multiresolution database?
• How can the map objects in two levels of detail be matched and the enriching relations and their attributes be gained?
• How can the relations and the matching process be managed and the relations be deployed?
• Can these vertical relations be used for the creation of intermediate levels of details?
• Can the same relations also be used for incremental Generalization?
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2.2 Classifying and Modeling Relations
Classification
of Relations
Modeling
of Relations
Extraction
of Relations
Storage
of Relations
Exploitation
of Relations
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Vertical Relations
procedural knowledege is bound to algorithm & scale
vertical relations = abstract knowledge express the geometrical, topological and semantical outcome formalize the outcome by parameterizing abstract generalization
operators
VerticalRelations
map-objectrelations
LODrelations
identity relation(micro object)
group relation(meso object)
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Vertical Relationsvertical relations
map object relations LOD relations
semantic structural
neigbourhood matrix
diversity
configuration
similarity
legend
type priorities
causal & logic
identity relation 1:1(micro object)
group relation n:m(meso object)
simplification *
smoothing *
enlargement *
exaggeration *
collapse *
aggregation *(alignment, cluster)
amalgamation *(cluster)
typification *(cluster, alignment)
symbolization *
displacement *
partitioning *
(through e.g. alignments)
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Vertical Identity Relations 1:1
simplification
smoothing
enlargement
exaggeration
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Vertical Identity Relations 1:1
collapse
symbolization
displacement
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aggregation
Vertical Group Relations n:m
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typification
amalgamation
Vertical Group Relations n:m
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Relation Properties
relation properties
semantic properties geometric properties topological properties
size / position
shape
orientation
neigbourhood
intersection type
structure
statistics
resistance /attraction
configuration(island, landscape)
containment(in, ring model)
change originator
threshold level
type change
color codes for properties:
valid for identity relations
valid for group relations
valid for all relations
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Relation Properties
Topology, compactness
Frequency, distance, size
Inter-thematic (riversoil)
Orientation, meso structure
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2.3 Extracting Relations
Classification
of Relations
Modeling
of Relations
Extraction
of Relations
Storage
of Relations
Exploitation
of Relations
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Matching
1:25‘000 1:200‘000
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Matching
The main possibilities of the matching process:
• semantic matching
(e.g. by object name or identifier)
• geometric matching
(e.g. by location, size,
surface description)
• topological matching
(e.g. overlaps,
neigbourhoods)
relation properties
semantic properties geometric properties topological properties
size / position
shape
orientation
neigbourhood
intersection type
structure
statistics
resistance /attraction
configuration(island, landscape)
containment(in, ring model)
change originator
threshold level
type change
relation properties
semantic properties geometric properties topological properties
size / position
shape
orientation
neigbourhood
intersection type
structure
statistics
resistance /attraction
configuration(island, landscape)
containment(in, ring model)
change originator
threshold level
type change
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Matching – Properties
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2.4 Storing & Representing Relations
Classification
of Relations
Modeling
of Relations
Extraction
of Relations
Storage
of Relations
Exploitation
of Relations
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Storing & Representing Relations
How to …• represent and store the vertical relations in a MRDB
(relation objects, attributes …)?• represent identity, group relations and special cases?• establish links to the horizontal relations (Stefan Steiniger)?• represent interdependencies with horizontal relations?• make the relations (as support service) available to others?
tree structure ?
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Representing Relations
Current MRDB approaches usually work with strictly hierarchical data structures such as aggregation trees
not flexible enough
evaluation of non-taxonomic and partonomic relations
Database technology:
OODBMS vs. RDBMS
elegance vs. performance
directed acyclic graph (DAG)
?
RDBMS
OODBMS
from www.gitta.info
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Managing and Deploying Relations
Open Generalization Platform with Web-Services technology
Auto-Carto 2005
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Application scenarios
Web Feature Service
Middleware solution
Generalization Service
Web Map Service
GEO Database
http://
GIS Client / Browser
• clustering allows real time typification of symbolized foreground objects (e.g. points of interest)
• applications - adaptive zooming for web mapping- dynamic mapping for mobile applications
• limits: only applicable for simple generalization operations
Generalization platformGIS, map production Generalization
Service
• standalone generalization services• interactive solution, generalization service as toolbox• practicable for complex generalization• applicable in advance, e.g. semi automated update
Auto-Carto 2005
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Open Research Platform
map production• possibility for small companies to offer generalization solutions,
new business models• customers can keep their production lines
open research platform for generalization• allows techniques and code to be shared• supports benchmarks and comparison of different
implementation• complex generalization task like orchestration of generalization
operators can be addressed• at the last meetings of “ICA Commission on Map Generalization
and Multiple Representation” (Paris 2003 and Leicester 2004) University Zurich got responsibility to bring forward the idea ofa common open research platform for generalization
Auto-Carto 2005
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Open Research Platform
Registry for
Generalization Services
Generic
XML Interface
Descriptions
Auto-Carto 2005
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2.5 Exploiting Relations
Classification
of Relations
Modeling
of Relations
Extraction
of Relations
Storage
of Relations
Exploitation
of Relations
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Exploiting Relations
• interpolation of intermediate scale levels (e.g. Morphing)
• incremental generalization and updating
• ...
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Morphing
Morphing of single points along linear or weighted transformation paths:
• Every point in LOD1 has a transformation path to the final point in LOD2
• The intermediate point is created by simple interpolation along the transformation path
• Interpolation can be realized directly in the database (stored procedures)
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Morphing
Combining vector morphing with scaleless storage of the geometry.
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Time Table, Conferences & Publications
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Conferences & Publications
• ICA Workshop 2004: Neun, M., R. Weibel and D. Burghardt,
Data Enrichment for Adaptive Generalisation
• Auto-Carto 2005: Burghardt, D., M. Neun and R. Weibel,
Generalization Services on the Web – A Classification and an Initial Prototype Implementation
• ICA Book 2005: Edwardes, A., D. Burghardt and M. Neun,
Experiments to build an open generalisation system
also in a CaGIS Special Issue
• ISGI Symposium 2005: Edwardes, A., D. Burghardt and M. Neun,
Interoperability in Map Generalisation Research
• ICA Workshop 2005: Neun, M. and D. Burghardt,
Web Services for an Open Generalisation Research Platform
• ICA Conference 2005: Neun, M. and S. Steiniger,
Modelling Relations for Categorical Maps
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Time Table
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Conclusion
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Conclusion
Purpose: • data enrichment• modeling of enriched data• exploitation of enriched data
Focus: • thematic vector maps
Goals/Questions: • types of “vertical” relations betweenmap objects on different LODs?
• modelling and representing in a MRDB?• matching of map objects in two LODs and
acquisition relations and their attributes?• management and deployment of relations?• usefulness of vertical relations for the
creation of intermediate LODs?• usefulness of the same relations for
incremental generalization?
Classification
of Relations
Modeling
of Relations
Extraction
of Relations
Storage
of Relations
Exploitation
of Relations
Classification
of Relations
Modeling
of Relations
Extraction
of Relations
Storage
of Relations
Exploitation
of Relations
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Thanks for your attention!
Any questions, suggestions or comments?
Bibliography:
Cecconi, A. (2003) Integration of Cartographic Generalization and Multi-Scale Databases for Enhanced Web
Mapping
Galanda, M. (2003) Automated Polygon Generalization in a Multi Agent System
Kilpelainen, T. and T. Sarjakoski (1995)Incremental Generalization for Multiple Representations of Geographical Objects
Ruas, A. (1999)Modèle de généralisation de données géographiques à base de contraintes et d‘autonomie
Weibel, R., S. Keller and T. Reichenbacher (1995)Overcoming the Knowledge Acquisition Bottleneck in Map Generalization: The Role of InteractiveSystems and Computational Intelligence.
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Full Bibliography
Bobzien, M. and D. Morgenstern (2002), Geometry Change in Model Generalization – A Geometrical or a Topological Problem
Kilpelainen, T., Sarjakoski, T. Incremental Generalization for Multiple Representations of Geographical Objects. In Muller, J. C., Lagrange, J. P., Weibel, R. (editors) GIS and Generalization: Methodology and Practice, Taylor & Francis, 1995.
Weibel, R., S. Keller and T. Reichenbacher (1995). Overcoming the Knowledge Acquisition Bottleneck in Map Generalization: The Role of Interactive Systems and Computational Intelligence. In: Frank, A.U.; Kuhn, W. (eds.): Spatial Information Theory: A Theoretical Basis for GIS. Lecture Notes on Computer Science, Berlin: Springer-Verlag, Vol 988: pp. 139-156