A Data Fusion System for Spatial Data Mining, Analysis and Improvement Silvija Stankute, Hartmut Asche - University of Potsdam

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Summary Data fusion system for spatial data mining

1. Motivation
2. Concept: Automated data fusion
3. System architecture: Generic components
4. Fusion pipeline: Operations and workflow
5. System operation: User interface
6. Conclusion


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1 Motivation Improvement of geodata quality
 Acquistion of geodata by range of actors including state insti-
tutions (NMAs) and private enterprises resulting in heteroge-
nous, frequently redundant geospatial databases
 Geometric, semantic quality of geospatial data heterogenous,
frequently insufficient or inaccurate: unreliable data quality of
existing datasets for identical real world section
 Effective geodata management and use necessitate harmonisa-
tion of heterogenous geodata according to application-specific
data quality specifications
 To avoid fresh data acquisition automated process required to
fuse imperfect geometric and/or semantic information of 2 or
more datasets to produce optimal application-specific data


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2 Concept Automated fusion of imperfect geodata

1 2

1+2 1+2

3 1+2+3


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2 Concept Automated fusion of imperfect geodata
 Development and implementation of automated fusion process
(DataFusion) to produce single geospatial dataset from existing
datasets superior in geometric and/or semantic quality to im-
perfect source data
 Objective to extract, filter and combine relevant features from
diverse source data into single best-fit quality dataset
according to user and application specifications
 Data harmonisation and fusion process allows for selection,
elimination and/or substitution of unwanted source attribute
features by user-specified geometric and/or semantic attributes
 DataFusion or DAFU provides user-defined data filter to gene-
rate optimal geodata in automated filtering process


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3 System architecture Modular components


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3 System architecture Modular component system
 Implementation of DataFusion based on generic, modular com-
ponent architecture and object-oriented, procedural cross-plat-
form programming language (Perl)
 Presently DataFusion consists of 3 components, sequentially
linked in fusion pipeline
 Preprocessing component: preprocessing modules for Tele-
atlas, Navteq, ATKIS input data, at present
 Filtering/fusion component: merge of 2 or more different
input datasets into single optimal dataset
 Validation component: quality assessment of merged dataset
according to user or application specifications


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4 Fusion pipeline Preprocessing of source data

Source
data

Conversion to 1 2
Conversion to
uniform coordinate uniform data
system format
Quality measures

3 4
3 5 6
Analysis for
Analysis for topological Analysis for topo- Analysis for
uniqueness 2 logical errors completeness
completeness

7
Geometric
correction 6

Preprocessed
input data


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4 Fusion pipeline Preprocessing of source data
 Preprocessing component executes the following operations on
heterogenous geospatial source data:
 Objective: Quality assessment of input vector data model
underlying each source dataset
 Operations: Selection of source data; integration of source data
by conversion to unified coordinate system; transformation into
common data format; source data assessment for uniqueness
and completeness; quality assessment and adjustment of topo-
logical correctness, thematic completeness
 Result: Preprocessed input datasets used as input data for sub-
sequent fusion/filtering component


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4 Fusion pipeline Fusion of preprocessed data
Detection of 1
Preprocessed
input data relations among
input data

2
Assignments of
related objects

3 4
3
Transfer of geo- Transfer of the-
metric information matic information 2

Merged
output data


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4 Fusion pipeline Fusion of preprocessed data
 Data filtering/fusion component executes following operations
on preprocessed geospatial input data:
 Objective: Generation of single optimal dataset by transmission
and augmentation of attribute features from n input datasets
 Operations: Iterative comparison of geometric features (coor-
dinates) of vector input datasets; determination of relationships
between data features and real-world objects; generation of
non-redundant fusion data (1 semantic feature assigned 1 geo-
metric feature only, vice versa); transfer (cross-referencing)
and extension of specified attributes to target dataset
 Result: Merged dataset used as input data for subsequent vali-
dation component


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4 Fusion pipeline Validation of merged data
1
Merged Validation of
output data fusion quality

2
Interactive error
correction

3
Coordinate system
transformation

4
3
Data format con-
version 2

Specified
DAFU data


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4 Fusion pipeline Validation of merged data
 Validation component executes the following operations on
single merged geospatial dataset:
 Objective: Quality verification of fusion process
 Operations: Calculation and evaluation of data fusion quality;
if required and/or specified: interactive correction of errors of
source data (< 5 percent for linear objects, <10-15 percent for
polygonal objects); transfer of merged geodata to specified co-
ordinate systems; conversion of merged dataset into specified
data formats (SVG, CSV, SHP, etc.)
 End result: Application and/or user-specified optimal
geospatial dataset


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5 System operation User interface
 Front-end of Data Fusion system allows for 2 operation modes:
graphical user interface (GUI) or command-line interface
 Command-line operation for implementation into remote sys-
tems, such als servers, clusters, etc., by GI experts
 GUI operation standard operation mode for application-orien-
ted GI users
 GUI composed of 8 widgets covering core funtions of DAFU;
widgets communicate via data exchange and signal exchange
(bindings)
 Additional flexible support system provides user with relevant
information on operation and understanding of DAFU


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5 System operation User interface > GUI
 Abb 5-3 Diss


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6 Conclusion Data fusion – what‘s the benefit?


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6 Conclusion Data fusion – what‘s the benefit?
 The DataFusion system presents an innovatiove approach to
geospatial data mining by harmonising and improving the geo-
metric and semantic quality of digital vector data
 DAFU demonstrates that single optimal geospatial data can be
generated from existing suboptimal datasets making repeated
data acquistion unneccessary
 DAFU facilitates cost-effective geospatial data management by
multiple re-use of existing datasets customised to individual
user and/or application requirements
 DAFU contributes to reducing heterogeneity and redundancy of
geospatial data in geo databases, at the same time increasing
efficient, meaningful use of geographically-related mass data


A Data Fusion System for Spatial Data Mining, Analysis and Improvement Silvija Stankute, Hartmut Asche - University of Potsdam

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A Data Fusion System for Spatial Data Mining, Analysis and Improvement Silvija Stankute, Hartmut Asche - University of Potsdam

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