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Spatial OLAP for environmental data: solved and unresolved problems Sandro Bimonte – Research Centre on Tecnologies, information systems and processes for agriculture (TSCF), Clermont Ferrand ( France )
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Spatial OLAP for environmental data: solved and unresolved problems Sandro Bimonte – Research Centre on Tecnologies, information systems and processes for agriculture (TSCF), Clermont Ferrand ( France )


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Spatial OLAP for environmental data: solved and unresolved problems …

Spatial OLAP for environmental data: solved and unresolved problems
Sandro Bimonte – Research Centre on Tecnologies, information systems and processes for agriculture (TSCF), Clermont Ferrand ( France )
Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)

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  • 1. Geographic OLAP: from Modelling to Visualization Sandro Bimonte TSCF, CEMAGREF, Clermont-Ferrand, France
  • 2. Outline  Context  Geographic information and Spatial analysis  Data Warehouse and OLAP  Spatial OLAP  Contributions  Modelling  Geographic OLAP  GeoCube: conceptual model  Visualization  GeWOlap: a Web-based Geographic OLAP Tool  GeOlaPivot Table: a 3D visualization and interaction methaphor  GoOLAP: integration of Geovisualization and OLAP tools  Perspectives  Conclusions S4 ENVISA Workshop 19/6/2009 2/38
  • 3. Context Geographic information  Geographic information is the representation of an object or a real phenomenon located in the space  It is characterized by  Spatial component: position and the shape  Semantic component:  Information about the nature, the aspect and the other descriptive properties  Spatial, thematic and/or cartographic generalization relationships with other objects or phenomena S4 ENVISA Workshop 19/6/2009 3/38
  • 4. Context Spatial Analysis  Spatial analysis process is flexible and iterative Identify the problem Select tools Layer A Input Identify data Spatial operation Create and analysis plan Layer B Spatial operation Show results Layer C Output Examine results Change parameters Redefine the process S4 ENVISA Workshop 19/6/2009 4/38
  • 5. Context Data Warehousing and OLAP (1/2)  A data warehouse is "a subject-oriented, integrated, non- volatile and time-variant collection of data stored in a single site repository and collected from multiple sources" [Immon92]  Data warehouse models are designed to represent measurable facts, described by measures, and the various dimensions that characterize the facts and represent analysis axes Location Time An instance of a multidimensional model is an hypercube Year Store City  Month Name Code_year Code_Month Name Code Label Label Population Address  OLAP tools implement interactive analysis techniques used Sales to rapidly explore the data warehouse through OLAP Type Clients operators Products Code Client Label Item Volume : SUM Name Code Age Name Brand Price Name Code S4 ENVISA Workshop 19/6/2009 5/38
  • 6. Context Spatial OLAP  Spatial OLAP (SOLAP)  "A visual platform built especially to support rapid and easy spatio-temporal analysis and exploration of data following a multidimensional approach comprised of aggregation levels available in cartographic displays as well as in tabular and diagram displays“ [Bédard97]  Cartographic representation of the multidimensional data allows :  Visualize spatial distribution of the facts  Visualize (spatial) relationships between facts and classical dimensions  Visualize facts at different spatial granularities S4 ENVISA Workshop 19/6/2009 6/38
  • 7. Context Main Spatial OLAP Concepts  Spatial Dimension:  Spatial non geometric (i.e. text only members)  Spatial geometric (i.e. members with a cartographic representation)  Mixed spatial (i.e. combining cartographic and textual members)  Spatial Measure:  List of spatial objects  Result of spatial operators Road Coating Geo Location City Quarter State Coating Calendar Month  Spatio-multidimensional operators Insurance Insurance Type Insurance Name Time Name Name Population Number Category Type Name Population Area  Navigate into spatial dimension (Roll-Up/Drill-Down) Number Date_day Durability Year Name Validity period Highway Week  Slice the hypercube Manteinance Time Year Accidents Highway Structure Date Week number Highway Highway Date Highway Age Category Section Segment Event Season Age Group Client Segment number Name Section number Length(S) First name Road Condition No. Cars Group name Last name Repair Cost Min value Amount paid Age Max value Location /GU Position S4 ENVISA Workshop 19/6/2009 7/38
  • 8. Context Spatial OLAP: Tools Rivest, et al. 05 Scotch, et al. 05 Webigeo Voss, et al. 04 S4 ENVISA Workshop 19/6/2009 8/38
  • 9. Context Spatial OLAP Limits Geographic SOLAP Information Dimension Spatial Map Hierarchy Generalization Relationships Semantic Measure Spatial Descriptive component Component Attributes Analysis Axes and Data creation/ subject modification defined a Flexibility priori S4 ENVISA Workshop 19/6/2009 9/38
  • 10. Geographic OLAP S4 ENVISA Workshop 19/6/2009 10/38
  • 11. Contribution: Geographic OLAP Geographic Dimension  A dimension is geographic if the members at least of one level are geographic objects S4 ENVISA Workshop 19/6/2009 11/38
  • 12. Contribution: Geographic OLAP Descriptive Hierarchy  A descriptive hierarchy is defined using descriptive attributes of objects Hiérarchie descriptive Time Lagoon Year Month Day Unit Type Name Year Month Day Plants Area Name Type Pollution Salinity Pollutants CarbonsAtomsNum TypeP BoundsType Pollutant ber Code All_units Name Name Rate : AVG Cbn_code Bt_code Density BoilngPoint Commercial Industrial Mazzorbo Ancora Chioggia Romea S4 ENVISA Workshop 19/6/2009 12/38
  • 13. Contribution: Geographic OLAP Spatial Hierarchy  A spatial hierarchy if a hierarchy where members of different levels are related by topological inclusion and/or intersection relationships Hiérarchie spatiale Time Lagoon Year All_units Month Day Unit Zone Name Year Month Day Plants Name Area Area Type Pollution Salinity Bocca North Swam Bocca Chioggia South Swam Lido Pollutants CarbonsAtomsNum TypeP BoundsType Pollutant ber Code Canal Name Rate : AVG Carbonera Name Mazzorbo Cbn_code AncoraBt_code Choggia Romea Density Ronzei Figheri Bissa BoilngPoint S4 ENVISA Workshop 19/6/2009 13/38
  • 14. Contribution: Geographic OLAP Generalization Hierarchy  A hierarchy is a Generalization hierarchy if:  members represent the same geographic information at different scales  members of a level are the result of generalization of members of the directly inferior level All_units Lagoon Time Unit 1:1500 Unit 1:500 Year Month Day Name Name Plants Plants year month Area Area day Sacco Ghebo Botta Sora Type Salinity Salinity Storto Canal-Treporti Pollutants Carbons Bounds Type Atoms Pollutant Unità Type Pollution Number Barenali name Cbn_code Code Bt_code Name Paleazza Sacco Ghebo Density Storto Botta Sora BoilingPoint Treporti Canal Rate: Avg S4 ENVISA Workshop 19/6/2009 14/38
  • 15. Contribution: Geographic OLAP Geographic Measure  A geographic measure is a geographic object which can belong to one or more hierarchy schemas Time Rate Year Month Day Rate5 Rate10 Year Month Day Value5 Value10 Pollution Pollutants CarbonsAtomsNum TypeP BoundsType Pollutant ber Code Name Name Unit Cbn_code Bt_code Density BoilngPoint Geom : Fusion Name : No Aggregation Plants : List /Area Type : Ratio Salinity : AVG S4 ENVISA Workshop 19/6/2009 15/38
  • 16. Contribution: Geographic OLAP Multidimensional Operators  Drill and slice operators And…  Operators which dynamically modify spatial dimensions  Operator to permute measure and dimension  Operators to navigate into hierarchy measure S4 ENVISA Workshop 19/6/2009 16/38
  • 17. GeoCube S4 ENVISA Workshop 19/6/2009 17/38
  • 18. GeoCube  Entity Schema et Instances model members and measures  Entity Schema et Instances are organized into hierarchies (Hierarchy Schema et Instance)  Base Cube represents the fact table where all dimensions are at the most detailed levels  Every level can be used as dimension or as measure  A measure belongs to a hierarchy  Aggregation Mode defines aggregations for the entity used as measure  View represents a multidimensional query S4 ENVISA Workshop 19/6/2009 18/38
  • 19. Contribution: GeoCube Algebra  Let Vv = 〈BCbc, L, Θk, γ〉 then Op (Vv) [parameters] = V’v = 〈BC’bc, L’, Θ’k, γ’〉 where γ’ is calculated using an algorithm Navigation Modification Roll-Up Permute Slice OLAP-Buffer Dice OLAP-Overlay Classify Specialize S4 ENVISA Workshop 19/6/2009 19/38
  • 20. Contribution: GeoCube Properties Data modelling properties Damiani Jensen Ahmed Pourabbas GeoCube Set of measures OK NO OK NO OK Dimension attributes NO NO NO OK OK Multi-valued measures OK OK OK OK OK User-defined aggregation OK OK NO OK OK functions Derived measures NO NO NO NO OK (derived dimension attributes) N-n relationships between NO OK NO OK OK dimensions and facts Complex hierarchies OK OK NO OK OK Correct Aggregation of NO NO NO NO OK Geographic measures Imprecision of Multi-association NO NO NO NO OK relationships for Map Generalization hierarchies S4 ENVISA Workshop 19/6/2009 20/38
  • 21. Contribution: GeoCube Properties Spatio-multidimensional Damiani Jensen Ahmed Pourabbas GeoCube Operators Operators which modify NO NO NO NO OK spatial dimensions Permute NO OK NO NO OK Navigation into measures Part Part NO NO OK hierarchy (Multigranular analysis) S4 ENVISA Workshop 19/6/2009 21/38
  • 22. GeWOlap S4 ENVISA Workshop 19/6/2009 22/38
  • 23. Contribution GeWOlap  Web Geographic OLAP tool:  OLAP-GIS integrated  Synchronized environment  Geographic measures and dimensions  Geographic OLAP operators S4 ENVISA Workshop 19/6/2009 23/38
  • 24. Contribution: GeWOlap Architecture OLAP Client JPivot MapXtreme Java + Tabular Display Cartographic display OLAP Server Cube definition <Schema name=pollution> <AggName name=agg_1_poll> Mondrian …. <Cube name=Pollution> Pollution.xml … </Cube> Spatial Data Warehouse Aggregate Tables Spatial Tables Spatial ORACLE Dimensions and facts tables S4 ENVISA Workshop 19/6/2009 24/38
  • 25. Contribution: GeWOlap User Interface GIS operators Geographic OLAP operators S4 ENVISA Workshop 19/6/2009 25/38
  • 26. Contribution: GeWOlap Geographic Measures S4 ENVISA Workshop 19/6/2009 26/38
  • 27. Contribution: GeWOlap Drill-down Position S4 ENVISA Workshop 19/6/2009 27/38
  • 28. Contribution: GeWOlap OLAP-Overlay Depuration Map S4 ENVISA Workshop 19/6/2009 28/38
  • 29. GeOlaPivot Table S4 ENVISA Workshop 19/6/2009 29/38
  • 30. Contribution GeOlaPivot Table  GeOlaPivot Table is a 3D interaction metaphor  Combines Space-Time Cube and Pivot Table concepts  A third dimension provides an insight of spatial evolution of the phenomenon in function of other inputs (time, products) using the map overlay  Visually compare spatial relationships between measures of different members of the same level  Visualize spatial relationships between measures and dimensions members  Visual representation of the structure of the multidimensional application  OLAP operators through the simple interaction S4 ENVISA Workshop 19/6/2009 30/38
  • 31. Contribution: GeOlaPivot Table Mock-up S4 ENVISA Workshop 19/6/2009 31/38
  • 32. GoOLAP S4 ENVISA Workshop 19/6/2009 32/38
  • 33. GoOLAP  It combines the facilities provided by a commonly used geobrower and a traditional OLAP system  It integrates in a web application, the 3D capabilities provided by the geobrowser Google Earth with a freely available OLAP server, Mondrian  The main advantage of this solution is to provide a web-based SOLAP environment, able to render in 3D spatial data  Date can be provided by different (remote) data repositories.  The Decision Maker can highly personalize the visual encodings of the information S4 ENVISA Workshop 19/6/2009 33/38
  • 34. Contribution: GoOLAP User Interface S4 ENVISA Workshop 19/6/2009 34/38
  • 35. Current work  Introduction of continuous field data into SOLAP  Aggregation by means of Map Algebra  Definition of visual language for Spatial Data Warehouse  Spatial Data Warehouse using semi- structured data (GML) S4 ENVISA Workshop 19/6/2009 35/38
  • 36. Future Work  Modelling  SOLAP Conceptual Model for sensor network data  Introduction of Spatio-temporal multigranular data in SOLAP  Definition of new operators which modify dynamically spatial dimensions  Integrity constraints for Spatial Data Warehouse  Introduction of vague spatial data in SOLAP  Visualization  Introduction of temporal component in GoOLAP S4 ENVISA Workshop 19/6/2009 36/38
  • 37. Conclusions (1/2)  Spatial OLAP integrates spatial data in OLAP systems  SOLAP models and tools do not “well” handle geographic data and spatial analysis  A new multidimensional analysis paradigm: Geographic OLAP S4 ENVISA Workshop 19/6/2009 37/38
  • 38. Conclusions (2/2)  Geocube: multidimensional model and algebra for Geographical OLAP  GeWOlap: web OLAP-GIS integrated solution based on GeoCube  GeOlaPivot Table: a visualization and interaction metaphor to analyze geographic measures  GoOLAP: a system wich integrates geovisualization and OLAP functionalities  New trends in SOLAP and Spatial Data warehousing S4 ENVISA Workshop 19/6/2009 38/38
  • 39. Questions for me…and You  How we can estimate missing values in SDW?  using hierachies ?  Is it possible to couple ML,DM algorithms with SOLAP ?  using hierarchies ?  How improve SOLAP visualization?  reducing dimensionality S4 ENVISA Workshop 19/6/2009 39/38
  • 40. Thanks for your attention Merci Grazie Questions ? S4 ENVISA Workshop 19/6/2009 40/38