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Quality issues in Spatial Databases M. Mostafavi, G. Edwards, R. Jeansoulin CRG & GEOIDE & REVIGIS Victoria, May 2003. Contents. Introduction Problems Objective Methodology Results Discussion Conclusions and perspectives. Introduction. Data fusion and Data Quality

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  1. Quality issues in Spatial DatabasesM. Mostafavi, G. Edwards, R. Jeansoulin CRG & GEOIDE & REVIGISVictoria, May 2003

  2. Contents • Introduction • Problems • Objective • Methodology • Results • Discussion • Conclusions and perspectives

  3. Introduction • Data fusion and Data Quality • Multi sources spatial data • Vector data : BNDT, BDTQ, … • Raster data: satellites images, aerial images,… • Need for better quality • Logical consistency • Completeness • Semantic accuracy • Temporal accuracy • Positional accuracy • and more … • Decision making (Effective crisis management (MSPQ))

  4. A real case problem • BNDT: good geometry • Statistics Canada database, Canada election database: reach descriptive information but weak geometry • How to reconcile these two data sets? BNDT SC, EC

  5. Context SDB1 Information of greater quality SDB2 Fusion SDB3 User vision (fitness for use) Producer vision (Product ontology)

  6. Logical consistency • Logical consistency is an important element of data quality. It defines the degree of consistency of the data with respect to its specifications. • Integrity constrains • Explicit rules stated in the data specifications (e.g. connectivity between two objects) • Implicit rules (e.g. a river always flows downstream) • Ontology vs. specifications Ontology specifications

  7. Project definition data Consistency vs. BNDT Yes Does this Help? ?......No Mapping the ontologies Ontology fusion Step 1 Step 3 Step 4 NTDB Ontology Integrated ontology BDTQ Ontology Ontology consistency Lack of explicit rules data consistency data consistency BDTQ data Step 2 New data set NTDB data Data fusion Step 5

  8. Consistency in NTDB Step 1 Step 2 NTDB Ontologies Dataset Delphi Interface Delphi Interface Prolog Studying the Logical consistency of the dataset

  9. Formalizing the ontology BNDT Ontology Knowledge base Rules Facts Queries

  10. A C D E B A B B B A A C Spatial relations in NTDB • Spatial relations in NTDB are: • Connection relations • Sharing relations • adjacency relations • Superposition relations 1 2 3 4

  11. Logical approach- facts • For NTDB the facts consist of • Taxonomy of NTDB • Themes • Entities • Allowed Combinations • Code (NTDB identity code) • Geometric representations • Spatial relations • Connection • Sharing • Superposition/ adjacency • Minimal values (e.g. distance constraints between objects)

  12. Logical approach- facts • There are about 350,000 facts describing the NTDB • Remark: regrouping of objects for programming purposes has created some inconsistencies

  13. Logical approach- rules Several rules are defined to analyze the ontological consistency of the NTDB. Inconsistency rules

  14. Results (1/2) Inconsistency (inverse connection) Data dictionary: (generic relation) • between themes:Railway(L) Connected toRoad(L) • between themes :Road (L) Connected to Railway(L) Table of connection and cardinalities ?

  15. Results (2/2) Inconsistency (Different Values for the cardinality one) Data dictionary: (Generic relation) Gas and oil facilities (P) is ConnectedtoBuilding (P) Table of connection and cardinalities ?

  16. Dataset VB Interface Consistency in Data Step 1 Step 2 NTDB Ontologies Delphi Interface Prolog Studying the Logical consistency of the dataset

  17. Geomedia professional Spatial operations • Meet • Entirely Contained • Entirely Contained by • Contains and • Contained by • Spatially equal • touch Meet Overlap

  18. Mapping Polygon – Polygon Relations

  19. Mapping problems • Several problems • Confusions in spatial relations • Unique mapping is not possible • Cardinalities cannot be considered

  20. Step 2: BNDT Data vs its ontology

  21. File 21E05 Region: Sherbrooke 68 Entities 23,283 objects Analyzed binary relations: Contours vs. water bodies Buildings vs. roads Water bodies vs. buildings Liquid depot vs. Liquid depot Roads vs. water bodies … Data vs ontology

  22. Results • Liquid depot vs. Liquid depot • Spatial representations (Point, Area) • Spatial relations • Ontology/ specification (superposition is illegal) • Data (superposition case is found)

  23. Results • Problem: Road crosses a water body • Illegal relation with respect to semantics of the objects • Incomplete ontology

  24. Results • Problem: Cut line crosses a water body • Illegal relation with respect to semantic definition of the objects • Incomplete ontology

  25. Results • Problem: Contour crosses water body • Illegal relation with respect to the ontology • Inconsistent data

  26. Results • Problem: Road crosses water body • Illegal relation with respect to the ontology • Inconsistent data

  27. Results • Problem: Road crosses Building • Illegal relation with respect to the semantics of objects • Incomplete ontology

  28. Results • Problem: Water body (L) superposed Vegetation (A) • Illegal relation with respect to the ontology • Inconsistent data • Control system problem

  29. Results • Problem: Buildings (S) superposed to water body (A) • Illegal relation with respect to the semantics of objects • Inconsistent data

  30. Results • Problem: Building (A) Overlap Vegetation (A) • Illegal relation with respect to the semantics of objects • Inconsistent data

  31. Suggestions, solutions • Adding new rules • Building (a) and vegetation (a) (illegal superposition) • Road (l) and building (conditional superposition) • A better control system is needed • Find exceptions

  32. Current situation • Product ontology is analyzed • Mapping of topological relations to binary relations • Ontology translation in prolog (Delphi program) • Consistency studding of spatial relations • Connection (table C) • Sharing (table D) • Superposition and adjacency (table E) • Consistency between different relations (fusion of facts) • connection and sharing , connection and superposition / adjacency, sharing and superposition / adjacency • Consistency of data vs. specifications are studied

  33. Future work • logical consistency of other available datasets • Mapping of ontologies • Fusion of ontologies • Fusion of data • Consistency of the newly created data set

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