Spatial OLAP for environmental data: solved and unresolved problems Sandro Bimonte – Research...
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Transcript of Spatial OLAP for environmental data: solved and unresolved problems Sandro Bimonte – Research...
Geographic OLAP: from Modelling to
Visualization
Sandro Bimonte
TSCF, CEMAGREF, Clermont-Ferrand, [email protected]
S4 ENVISA Workshop19/6/2009 2/38
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 Workshop19/6/2009 3/38
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
Context
S4 ENVISA Workshop19/6/2009 4/38
Spatial Analysis Spatial analysis process is flexible and
iterativeIdentify the problem
Examine results
Change parameters
Redefine the process
Select tools
Identify data
Create and analysis plan
Show results
Layer A
Layer B
Layer C
Spatial operation
Input
Output
Spatial operation
Context
S4 ENVISA Workshop19/6/2009 5/38
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
An instance of a multidimensional model is an hypercube
OLAP tools implement interactive analysis techniques used to rapidly explore the data warehouse through OLAP operators
Sales
Item
CodeNamePrice
Products
Store
NameCode
Address
Location
City
NamePopulation
Client
NameAge
Clients
Volume : SUM
Month
Code_MonthLabel
Time
Type
CodeLabel
Brand
NameCode
Year
Code_yearLabel
Context
S4 ENVISA Workshop19/6/2009 6/38
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
Context
S4 ENVISA Workshop19/6/2009 7/38
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
Spatio-multidimensional operators Navigate into spatial dimension (Roll-Up/Drill-Down) Slice the hypercube
Accidents
Insurance
NumberValidity period
Insurance Type
Time
Date_day
Calendar Month
Name
Amount paidLocation /GU
Client
First nameLast name
AgePosition
Age Category
Age Group
Group nameMin valueMax value
Insurance Category
NameWeek
Week number
Quarter
Number
Year
YearHighway
Manteinance
Coating
NameType
Durability
Road Coating
City
NamePopulation
Geo Location
State
NamePopulation
Area
Highway Segment
Segment numberRoad Condition
Highway Structure
HighwaySection
Section number Length(S)No. Cars
Repair Cost
Highway
Name
Date
DateEvent
Season
Time
Cardinalités
(1,1)
(1,N)
Niveaux
Nom du niveau
Attribut clé
Autres Attributs
Nom du niveau
Attribut clé
Autres Attributs
Critères d’analyse
Nom
Dimension
PointLigneSurface
Collection de PointsCollection de Lignes
AdjacentIntersectionDisjoint
A l’intérieurEgalCollection de Surfaces
A travers
A
B
C
Fait et Mesures
Nom du Fait
M esures
Cardinalités
(1,1)
(1,N)
Niveaux
Nom du niveau
Attribut clé
Autres Attributs
Nom du niveau
Attribut clé
Autres Attributs
Critères d’analyse
Nom
Dimension
PointLigneSurface
Collection de PointsCollection de Lignes
AdjacentIntersectionDisjoint
A l’intérieurEgalCollection de Surfaces
A travers
A
B
C
Fait et Mesures
Nom du Fait
M esures
Cardinalités
(1,1)
(1,N)
Niveaux
Nom du niveau
Attribut clé
Autres Attributs
Nom du niveau
Attribut clé
Autres Attributs
Critères d’analyse
Nom
Dimension
PointLigneSurface
Collection de PointsCollection de Lignes
AdjacentIntersectionDisjoint
A l’intérieurEgalCollection de Surfaces
A travers
A
B
C
Fait et Mesures
Nom du Fait
M esures
Context
S4 ENVISA Workshop19/6/2009 8/38
Spatial OLAP: Tools
Rivest, et al. 05 Scotch, et al. 05
Voss, et al. 04Webigeo
Context
S4 ENVISA Workshop19/6/2009 9/38
Spatial OLAP Limits
SOLAPGeographic Information
Dimension Spatial Hierarchy
Map GeneralizationRelationships
Measure Spatial Component
DescriptiveAttributes
Analysis Axes and subject defined a priori
Data creation/ modification
Semantic
component
Flexibility
Context
S4 ENVISA Workshop19/6/2009 10/38
Geographic OLAP
S4 ENVISA Workshop19/6/2009 11/38
Geographic Dimension
A dimension is geographic if the members at least of one level are geographic objects
Contribution:Geographic OLAP
S4 ENVISA Workshop19/6/2009 12/38
Descriptive Hierarchy
A descriptive hierarchy is defined using descriptive attributes of objects
Pollution
Day
Day
Time
Unit
NamePlantsAreaType
Salinity
Lagoon
Type
Name
Pollutant
CodeName
DensityBoilngPoint
Pollutants
BoundsType
Bt_code Rate : AVG
CarbonsAtomsNumber
Cbn_code
Month
Month
Year
Year
TypeP
Name
Hiérarchie descriptive
Ancora Chioggia Romea
Commercial Industrial
All_units
Mazzorbo
Contribution:Geographic OLAP
S4 ENVISA Workshop19/6/2009 13/38
Spatial Hierarchy
A spatial hierarchy if a hierarchy where members of different levels are related by topological inclusion and/or intersection relationships
Hiérarchie spatiale
Pollution
Day
Day
Time
Unit
NamePlantsAreaType
Salinity
Lagoon
Zone
NameArea
Pollutant
CodeName
DensityBoilngPoint
Pollutants
BoundsType
Bt_code Rate : AVG
CarbonsAtomsNumber
Cbn_code
Month
Month
Year
Year
TypeP
NameCanalBissa
Carbonera Mazzorbo Ancora
BoccaLido
North Swam
All_units
Choggia Romea Ronzei Figheri
Bocca Chioggia South Swam
Contribution:Geographic OLAP
S4 ENVISA Workshop19/6/2009 14/38
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
UnitàBarenaliPollution
Time
Day
day
Month
month
Year
year
Pollutants
Pollutant
CodeNameDensityBoilingPoint
Bounds Type
Bt_code
Carbons Atoms
NumberCbn_code
Type
name
Unit 1:1500
NamePlantsAreaTypeSalinity
Lagoon
Unit 1:500
NamePlantsAreaSalinity
Rate: Avg
Paleazza
Sacco GheboStorto
All_units
Botta SoraCanal
Botta SoraCanal-Treporti
TreportiSacco GheboStorto
Contribution:Geographic OLAP
S4 ENVISA Workshop19/6/2009 15/38
Geographic Measure
A geographic measure is a geographic object which can belong to one or more hierarchy schemas
Pollution
Day
Day
Time
Rate5
Value5
Rate
Rate10
Value10
Pollutant
CodeName
DensityBoilngPoint
Pollutants
BoundsType
Bt_code
CarbonsAtomsNumber
Cbn_code
Month
Month
Year
Year
TypeP
Name Unit
Geom : FusionName : No Aggregation
Plants : List/Area
Type : RatioSalinity : AVG
Contribution:Geographic OLAP
S4 ENVISA Workshop19/6/2009 16/38
Multidimensional Operators
Drill and slice operatorsAnd… Operators which dynamically modify
spatial dimensions Operator to permute measure and
dimension Operators to navigate into hierarchy
measure
Contribution:Geographic OLAP
S4 ENVISA Workshop19/6/2009 17/38
GeoCube
S4 ENVISA Workshop19/6/2009 18/38
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 Workshop19/6/2009 19/38
Algebra
Let Vv = BCbc, L, k, then
Op (Vv) [parameters] = V’v = BC’bc, L’, ’k, ’
where ’ is calculated using an algorithmNavigation Modification
Roll-UpSliceDice
ClassifySpecialize
PermuteOLAP-Buffer
OLAP-Overlay
Contribution:GeoCube
S4 ENVISA Workshop19/6/2009 20/38
Properties
Contribution:GeoCube
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 functions
OK OK NO OK OK
Derived measures(derived dimension attributes)
NO NO NO NO OK
N-n relationships between dimensions and facts
NO OK NO OK OK
Complex hierarchies OK OK NO OK OK
Correct Aggregation of Geographic measures
NO NO NO NO OK
Imprecision of Multi-association relationships for Map Generalization hierarchies
NO NO NO NO OK
S4 ENVISA Workshop19/6/2009 21/38
Properties
Spatio-multidimensional Operators
Damiani Jensen Ahmed Pourabbas GeoCube
Operators which modify spatial dimensions
NO NO NO NO OK
Permute NO OK NO NO OK
Navigation into measures hierarchy
(Multigranular analysis)
Part Part NO NO OK
Contribution:GeoCube
S4 ENVISA Workshop19/6/2009 22/38
GeWOlap
S4 ENVISA Workshop19/6/2009 23/38
GeWOlap Web Geographic OLAP tool:
OLAP-GIS integrated Synchronized environment Geographic measures and dimensions Geographic OLAP operators
Contribution
S4 ENVISA Workshop19/6/2009 24/38
Architecture
Spatial Data Warehouse
OLAP Server
OLAP Client
Spatial TablesAggregate Tables
Dimensions and facts tables
Spatial ORACLE
Mondrian<Schema name=pollution>
<AggName name=agg_1_poll>
….
<Cube name=Pollution>
…
</Cube>
Pollution.xml
Cube definition
Tabular Display
JPivot
+
MapXtreme Java
Cartographic display
Contribution:GeWOlap
S4 ENVISA Workshop19/6/2009 25/38
User Interface
GIS operators Geographic OLAP operators
Contribution:GeWOlap
S4 ENVISA Workshop19/6/2009 26/38
Geographic Measures
Contribution:GeWOlap
S4 ENVISA Workshop19/6/2009 27/38
Drill-down Position
Contribution:GeWOlap
S4 ENVISA Workshop19/6/2009 28/38
OLAP-Overlay
Depuration
Map
Contribution:GeWOlap
S4 ENVISA Workshop19/6/2009 29/38
GeOlaPivot Table
S4 ENVISA Workshop19/6/2009 30/38
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
Contribution
S4 ENVISA Workshop19/6/2009 31/38
Mock-up
Contribution:GeOlaPivot Table
S4 ENVISA Workshop19/6/2009 32/38
GoOLAP
S4 ENVISA Workshop19/6/2009 33/38
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
GoOLAP
S4 ENVISA Workshop19/6/2009 34/38
User Interface
Contribution:GoOLAP
S4 ENVISA Workshop19/6/2009 35/38
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 Workshop19/6/2009 36/38
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 Workshop19/6/2009 37/38
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 Workshop19/6/2009 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 Workshop19/6/2009 39/38
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 Workshop19/6/2009 40/38
Thanks for your attentionMerciGrazie
Questions ?