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National Survey and Cadastre – Denmark
Conceptual Modeling of Geographic Databases
- Emphasis on Relationships among Geographic Databases
Anders Friis-Christensen
National Survey and Cadastre
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 2
Outline
• Geographic data - Conceptual foundation
• Conceptual modeling- What and why
• Modeling geographic data• Modeling multiple representations• Summary
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 3
Geographic data (1)
• Data where spatial and temporal aspects are important for the intended applications
• Applications:- The spatial aspects appear as regions, lines, and
points, and changes occur discretely across time• Topographic, cadastral, and network applications
- The continuously changing spatial aspects• Environmental applications and location-based services
National Survey and Cadastre – Denmark
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 5
Geographic data (2)
• A geographic object has:- A spatial attribute value, which specifies the location in
space• Its data type can be, e.g., a polygon or a point
- Thematic attribute values, which specify thematic properties• Any data type (non-spatial data types)
- Temporal attribute values, which specify temporal properties:
• Valid time, vt, which specifies when something is valid. E.g., the color of a building was white from 1991-2000
• Existence time, et, which specifies when something exists. E.g., a building has existed from 1900-present
• Transaction time, tt, which specifies when something is being recorded as current in a database. E.g., in Aug. 2000 we record that John Doe was the owner of a building from Mar. 2000 to present. tt is (Aug, 2000, today), vt (Mar, 2000, today)
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 6
Geographic data (3)
• Associations among geographic objects:- Topological, e.g., that an address point is inside a
building- Metric, e.g., that a building is less than 10 meters
from a road- Part-whole associations, e.g., that a county consist of
several municipalities
• Constraints on geographic objects:- Constraints on objects. E.g., the size of an building
may not be smaller than 25 sqm- Constraints on associations. Eg., one object should be
inside another- Most model elements imply constraints, e.g., the data
type of attributes
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 7
Conceptual Data Models (1)
• The idealization of the world to be described • A way to organize and structure data• Common conceptual data models can:
- Support an Infrastructure for Geographic data, e.g., based on the work by ISO TC211
- Support reuse of solutions and designs (design patterns)
- Solve the problem of interoperability (exchange and querying of data)
- Give a clear overview and understanding of a given application (non-technical)
- Be used as a language between users, domain experts and developers
- Be modified and maintained easily- Be used as a documenting tool
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 8
Conceptual Data Models (2)
• Conceptual data models describe:- Object classes and their properties - Associations among object classes- Possible constraints on objects and attribute values
• Conceptual data models are:- Independent of later implementation
• Conceptual modeling notations:- Entity Relationship Model (E/R)- The Unified Modeling Language (UML)
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 9
System’s Development Context
Universe ofDiscourse
DBMS-specific
DBMS-independent
Requirements
ConceptualSchema
ConceptualData Modeling
Data ModelMapping
PhysicalDesign
LogicalSchema
InternalSchema
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 10
Example
• An example in UML:
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 11
Modeling Approaches (1)
• It is instructive to distinguish between two different approaches to providing better support for conceptual modeling of geographic data
• One approach is to extend the base notation- Spatio-temporal concepts are given special syntax- This makes for more compact diagrams- The modeling notation becomes more complex
• Another approach is to not extend base notation- With this approach, a library of generic diagrams is
offered- “Patterns” can be identified- Diagrams remain complex- Existing design and transformation tools are applicable
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 12
Modeling Approaches (2)
• One sub-approach of the extension approach is to introduce spatio-temporal annotations into the base notation:- In UML, which is extensible, stereotypes may be
defined
• A two-step design process can been advocated for the annotation approach:- First, a diagram is designed that models the
universe of discourse without taking into account the spatial and temporal aspects of the universe of discourse
- Second, the diagram resulting from the first step is annotated with spatio-temporal annotations
• Stereotypes:- Are used to define new semantics to existing model
elements- Notation: <<stereotype>> or as an icon- Example:
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 13
Modeling Geographic Data
• Example using extensions:
Stereotypes<<tempDependSpatial>> = Temporal dependent spatial object
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 14
Modeling Geographic Data (2)
• Underlying Spatial Model:
Open GIS Consortiumgeometry model:
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 15
Modeling Geographic Data (3)
• DBMSs (e.g., MySQL and Oracle) have adopted and implemented the OGC geometry model
• This means that we can map the spatial extension of the conceptual model directly to a logical and physical model
• No standardized model has been adopted for temporal aspects (several different implementations exist)
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 16
Outline
• Geographic data • Conceptual modeling• Modeling geographic data• Modeling multiple representations
- What it is and requirements- Conceptual modeling approach to integrate data- Multiple representation schema language
• Summary
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 17
• Multiple representation is when several databases describe the same entity
• The reasons for multiple representation vary- Different approaches in data collection- Different application purposes / definitions- Varying levels of detail (multi-scale)
Multiple Representation of Geographic Entities
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 18
Real-world entity
Motivation
Universe of discourse
DB1
DB3
DB2
• Integration of data:- May support new potential use of data for various analysis
purposes (e.g., integration of register and map data)- Can be used to rationalize the production of data
Integration depends on: - Definitions/semantics- Data models/structures- Varying levels quality
Applications 1 Applications 2
Applications 3
Road
Topographic Road
Networked Road
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 19
Example (Multi-Scale)
Topographic Map 1:50,000
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 20
Example (Multi-Scale)
Topographic Map 1:10,000
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 21
Example (Different Definitions)
TM Building id34225433342253423422534134225340
T10 Building id usage buildingType1798705 non-residential low houses
BR Building id usage numOfFloors timeCon31371004 apartment house 2 4731371003 store 2 4631371002 apartment house 2 4631371001 apartment house 2 46
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 22
• Problems today- Inconsistencies may occur among these multiple
representations- Less concern is given to the fact that data change- Maintaining spatial data is costly
• Benefits of a solution to handle MR- Reduce the cost of maintaining multiple
representations of an entity- Ensure that users are working with updated
representations- Possible integration of data comming from different
sources
Multiple Representation of Geographic Entities
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 23
Requirements
• An effective approach to the management of MR among legacy systems is needed
• Several requirements exist. We need to be able to:- Specify consistency, matching, and restoration rules- Evaluate consistency rules with respect to a multiply
represented entity- Match r-objects located in different representation
databases- Monitor the representation databases for changes - Restore consistency if inconsistency occurs- Translate the specification into a database schema- Keep the various legacy GISs autonomous
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 24
• Introducing an i-class has several advantages– No change in representation databases– A simple approach to describe complex
correspondence scenarios– A logical abstraction of an entity– Consistency, matching, and restoration requirements
can be expressed in a single class
• The MRSL is based on an ”Integration Class” (i-class), which abstracts the entity represented
Object correspondences (OCs)Value correspondences (VCs)
Multiple Representation Schema Language
Matching rulesRestoration rules
Existing classes in different databases, which we want to keep mutually consistent
attributes
i-class
attributes
r-class1
VC
OC
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 25
• The i-class is the main element in the MRSL and contains:- Attributes which are common for the r-classes- Consistency, matching, and restoration rules- Operations to restore consistency and match
objects
• A new stereotype is defined <<i-class>>, which specifies that a class:- Is defined within an integration database schema- Is associated to at least two representation classes- Should be identified uniquely- Has extra specification compartments for VCs,
matching rules, restoration rules
Integration Class in MRSL
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 26
Consistency Rules
• The consistency rules consist of:- Object correspondences (OCs), which specify
existence dependencies between the i-object and its r-objects
- Value correspondences (VCs), which specify value (attribute) dependencies between the i-object and its r-objects
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 27
Consistency Rules (OC)
• A new stereotype <<mr-association>> is defined for the OC:- It is always binary (either connects and i-class with
a r-class or connects two i-classes)- Its navigation is always from the i-class to the r-
class- It can be a master, i.e., the associated r-class
controls the instances- It is represented as a dash-dotted line
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 28
Consistency Rules (VC)
In UML the VCs are specified in a specification compartment and as initial values of i- attributes
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 29
• The matching rules specify how to find corresponding objects from different representation databases
• The following matching criteria can be used:- Attribute comparison, e.g., spatial- Global object identifier- Manual inspection
Matching Rules
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 30
Matching Process
Create i-object
No i-objecti-object id
Find r-object
Use lookup ormatching function Find i-object
Lookup
i-object id /r-objects id
Modify r-object /Establish correspondence
r-object id
Example:
Matching Rules: mr1: br.location inside tm.shape
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 31
Restoration Rules
• The restoration rules specify the restoration actions that need to be applied when an OC or VC is not satisfied
• The restoration rules follow the principle of event-condition-action (ECA) rules
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 32
Restoration Process
Object matching
r-object id
Check OC
i-object id /r-objects id
Restoration
false
Check VC
true
true
Event
false
Insert, update, delete, or notification
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 33
Restoration Rules
Example:
Restoration Rules: rr1: on insert tm then insert br {immediate}, rr2: on update tm.shape if not v1 then placeInside(br.location){immediate}
v1: br.location inside shape
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 34
Complete Schema
National Survey and Cadastre – Denmark Nordic Forum for Geo-statistics — March 25-26, 2004 35
Summary
• An overview of geographic data• Different conceptual modeling approaches• Standardized conceptual models support
- Standardized logical models and implementations- Integration and exchange of data
• Extensions can be used to satisfy those requirements posed by:- Geographic data in general- Multiple representations of geographic entities
• Extensions are solutions to capture special semantics
National Survey and Cadastre – Denmark
Thank you for your attention!
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