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D ata E nrichment for Adaptive Gen eralization from a Multiresolution Database Moritz Neun

D ata E nrichment for Adaptive Gen eralization from a Multiresolution Database Moritz Neun SNF-Project DEGEN 4/2004 - 4/2007. Context. DEGEN = D ata E nrichment for the Control of the Gen eralization Process (Stefan Steiniger) & D ata E nrichment for Adaptive Gen eralization

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D ata E nrichment for Adaptive Gen eralization from a Multiresolution Database Moritz Neun

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  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 Slides english Präsentation deutsch • Introduction • Data Enrichment• Defining Relations• Classifying and Modeling Relations• Extracting Relations• Representing Relations• Exploiting Relations • Time Table, Conferences & Publications • Conclusion Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  4. 1. Introduction Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  5. Generalization Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  6. Generalization Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  7. 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 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  8. Multiresolution Databases (MRDB) • Multiresolution ≠ Multirepresentation • Different Levels of Detail (LOD)are stored in one Database. • Common for web mappingservices (zooming) • Important for Generalization • Objects on different LODsare linked • Database Technologies • Object Oriented (e.g. Gothic) • (Object) Relational (e.g. ArcSDE) Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  9. 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 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  10. 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 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  11. Data Enrichment: 2.1 Defining Relations Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  12. 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. Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  13. 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. Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  14. Horizontal Relations • Presented last semester by Stefan Steiniger •  5 groups of measures for expressing horizontal relations Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  15. 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) Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  16. 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) • ... Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  17. Storage of Relations Modeling of Relations Extraction of Relations Classification of Relations Exploitation of Relations 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. Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  18. 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? Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  19. Storage of Relations Modeling of Relations Extraction of Relations Classification of Relations Exploitation of Relations 2.2 Classifying and Modeling Relations Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  20. 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 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  21. Vertical Relations Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  22. Vertical Identity Relations 1:1 simplification smoothing enlargement exaggeration Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  23. Vertical Identity Relations 1:1 collapse symbolization displacement Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  24. Vertical Group Relations n:m aggregation Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  25. Vertical Group Relations n:m amalgamation typification Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  26. color codes for properties: valid for identity relations valid for group relations valid for all relations Relation Properties Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  27. Relation Properties • Topology, compactness • Frequency, distance, size • Inter-thematic (riversoil) • Orientation, meso structure Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  28. Storage of Relations Modeling of Relations Extraction of Relations Classification of Relations Exploitation of Relations 2.3 Extracting Relations Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  29. Matching 1:25‘000 1:200‘000 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  30. 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) Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  31. Matching – Properties Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  32. Storage of Relations Modeling of Relations Extraction of Relations Classification of Relations Exploitation of Relations 2.4 Storing & Representing Relations Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  33. tree structure 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? ? Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  34. directed acyclic graph (DAG) 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 ? RDBMS OODBMS from www.gitta.info Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  35. Managing and Deploying Relations • Open Generalization Platform with Web-Services technology Auto-Carto 2005 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  36. http:// Application scenarios Web Feature Service Generalization Service GEO Database Web Map Service GIS, map production Generalization Service GIS Client / Browser Middleware solution Generalization platform • 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 • standalone generalization services • interactive solution, generalization service as toolbox • practicable for complex generalization • applicable in advance, e.g. semi automated update Auto-Carto 2005 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  37. 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 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  38. Open Research Platform Registry for Generalization Services Generic XML Interface Descriptions Auto-Carto 2005 Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  39. Storage of Relations Modeling of Relations Extraction of Relations Classification of Relations Exploitation of Relations 2.5 Exploiting Relations Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  40. Exploiting Relations • interpolation of intermediate scale levels (e.g. Morphing) • incremental generalization and updating • ... Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

  41. 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) Kolloqium Geographische Informationswissenschaft - Universität Zürich, 08.04.2005

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