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)

1. Geographic OLAP: from Modelling to
Visualization
Sandro Bimonte
TSCF, CEMAGREF, Clermont-Ferrand, France
Sandro.bimonte@cemagref.fr

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
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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
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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
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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
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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
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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
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8. Context
Spatial OLAP: Tools
Rivest, et al. 05 Scotch, et al. 05
Webigeo
Voss, et al. 04
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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
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10. Geographic OLAP
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11. Contribution:
Geographic OLAP
Geographic Dimension
 A dimension is geographic if the
members at least of one level are
geographic objects
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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
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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
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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
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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
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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
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17. GeoCube
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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
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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
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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
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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)
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22. GeWOlap
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23. Contribution
GeWOlap
 Web Geographic OLAP tool:
 OLAP-GIS integrated
 Synchronized environment
 Geographic measures and dimensions
 Geographic OLAP operators
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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
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25. Contribution:
GeWOlap
User Interface
GIS operators Geographic OLAP operators
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26. Contribution:
GeWOlap
Geographic Measures
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27. Contribution:
GeWOlap
Drill-down Position
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28. Contribution:
GeWOlap
OLAP-Overlay
Depuration
Map
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29. GeOlaPivot Table
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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
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31. Contribution:
GeOlaPivot Table
Mock-up
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32. GoOLAP
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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
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34. Contribution:
GoOLAP
User Interface
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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)
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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
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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
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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
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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
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40. Thanks for your attention
Merci
Grazie
Questions ?
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