d ata e nrichment for adaptive gen eralization from a multiresolution database moritz neun

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


Context

DEGEN

=

Data Enrichment for the Control

of the Generalization Process

(Stefan Steiniger)

&

Data Enrichment for Adaptive Generalization

from a Multiresolution Database

(Moritz Neun)


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


1. Introduction


Generalization


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


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)


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


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


Data Enrichment:

2.1 Defining Relations


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.


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.


Horizontal Relations

HorizontalRelations

Geometry Topology StructureStatistics &

DensitySemantics

Presented last semester by Stefan Steiniger

5 groups of measures for expressing horizontal relations


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)


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)

• ...


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


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?


2.2 Classifying and Modeling Relations

Classification

of Relations

Modeling

of Relations

Extraction

of Relations

Storage

of Relations

Exploitation

of Relations


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)


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)


Vertical Identity Relations 1:1

simplification

smoothing

enlargement

exaggeration


Vertical Identity Relations 1:1

collapse

symbolization

displacement


aggregation

Vertical Group Relations n:m


typification

amalgamation

Vertical Group Relations n:m


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


Relation Properties

Topology, compactness

Frequency, distance, size

Inter-thematic (riversoil)

Orientation, meso structure


2.3 Extracting Relations

Classification

of Relations

Modeling

of Relations

Extraction

of Relations

Storage

of Relations

Exploitation

of Relations


Matching

1:25‘000 1:200‘000


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


size / position

shape

orientation

neigbourhood

intersection type

structure

statistics




change originator

threshold level

type change

relation properties


size / position

shape

orientation

neigbourhood

intersection type

structure

statistics




change originator

threshold level

type change


Matching – Properties


2.4 Storing & Representing Relations

Classification

of Relations

Modeling

of Relations

Extraction

of Relations

Storage

of Relations

Exploitation

of Relations


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 ?


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


Managing and Deploying Relations

Open Generalization Platform with Web-Services technology

Auto-Carto 2005


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


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


Open Research Platform

Registry for

Generalization Services

Generic

XML Interface

Descriptions

Auto-Carto 2005


2.5 Exploiting Relations

Classification

of Relations

Modeling

of Relations

Extraction

of Relations

Storage

of Relations

Exploitation

of Relations


Exploiting Relations

• interpolation of intermediate scale levels (e.g. Morphing)

• incremental generalization and updating

• ...


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)


Morphing

Combining vector morphing with scaleless storage of the geometry.


Time Table, Conferences & Publications


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


Time Table


Conclusion


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


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.


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

d ata e nrichment for adaptive gen eralization from a multiresolution database moritz neun

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