The document discusses conceptual modeling for geoscience data. It describes how geoscience data is large, diverse, complex, volatile and interconnected. Conceptual modeling can help communicate data structures, simplify understanding, and enable data validation and system development. Benefits of conceptual modeling include aiding communication between stakeholders, automatic database generation, project management and transforming models into ontologies to improve data integration and discovery. Examples discussed include models from the British Geological Survey and case studies of conceptual modeling for the BExIS data management system.

1. Added Value of Conceptual Modeling in
Geosciences
Tayebeh Kiani, Javad Chamanara
February 2016
Tehran, Iran

2. Earth
2Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
• Earth is a complex system

3. Geosciences
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• The science of Earth is complicated…
Hence, the data!

4. Data in Geosciences
• Data in Geoscience is VERY
– Big
– Diverse
– Complex
– Volatile
– Inter-connected
• Look at
– EPA
– USGS
– OneGeology
– GEON
– EarthCube
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5. Paradigm Shift
• From:
– Experimental
– Theoretical
– Computational
• Data Intensive Science has emerged!
– Doing science by analyzing data
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6. Modeling
• A representation of:
– Process
– Concept
– Operation
of a System
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7. Modeling
• Representation often implies
– Simplification
– Easy Understanding
– Easy Validation
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8. Data Model
• Representation of a system in term of:
– Entities
– Relationships
– Data Flows
– Workflows
Analogous to Geographic maps
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9. Data Modeling
• The process of creating a data model
• For an information system
• By applying formal techniques
• Using proper tools (usually)
Analogous to Cartography
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10. Data Modeling in Geoscience
“A rock is a naturally occurring
solid aggregate of one or more
minerals or mineraloids”– Wikipedia
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Samples of rocks on
Earth and Mars
Should it be natural?
Can’t it be soft?Aggregate OR Composite?What about the proportion
of minerals?

11. Data Modeling in Geosciences
• British Geological Survey
– Open Geological Data Models
• Geochemistry Data Model
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SITE
SAMPLE
BATCH
ANALYSIS
ANALYTE_
DETERMINATION_
LIMITS
ANALYTE_
DETERMINATION
DIC_
Laboratory
DIC_
Analysis_Method
DIC_
Analysis_
Preparation
DIC_
Analyte
Sample_Ids:
A
B
C
Batch_Ids:
X
Y
Sample_ID, Batch_Id:
A,X
B,Y

12. • NADM Conceptual Model 1.0
• Geologic concept hierarchy
The Geologic Map of NADM
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13. GeoSciML Australia
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14. GSI Iran
• I know of a lot work done
– Unclear licensing!
– Not published!!
• So…
– Not Accessible!
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15. Applications
• Organizational Information Architecture
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16. Applications
• Information System Development
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17. Applications• Communication Medium
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18. Modeling Aspects
• Structural Modeling
objects, their classifiers,
relationships, attributes and
operations
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19. Modeling Aspects
• Behavioral Modeling:
Anything that changes
the objects, events,
sequences or
operations, and object
interactions
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20. Modeling Aspects
• Flow Modeling
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21. Modeling Approaches
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22. Network/Graph Data Model
• Water Grid Modeling
• Process Modeling
Modeling Approaches
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• RDF
• Graph Databases
• Neo4J
• IBM System G
• Info Grid

23. Modeling Approaches
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USGS Lithology
Relational Data Model

24. Data Container
Extended Property
Globalization Info«enumeration»
Measurement Scale
0..1
1
{No Duplicate}
Data Container
Extended Property
Globalization Info«enumeration»
Measurement Scale
0..1
1
{No Duplicate}
Data Container
Data AttributeMetadata Attribute
{No Extended Property}
Data Container
Data Type Unit
0..1
+Applies To
1
Data Container
Data Type Unit
0..1
+Applies To
1
Data Container
Methodology
Aggregate Function
0..1
Data Container
Methodology
Aggregate Function
0..1
Data Container Constraint
Default Value
Domain Value
Validator
Data Container Constraint
Default Value
Domain Value
Validator
Data Container
Semantic
Description
Data Container
Semantic
Description
Modeling Approaches
Object Oriented Modeling
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25. Modeling Techniques
• ERDs:
– Are mostly relational
– Do not capture behaviors
– Do not capture processes and sequences
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26. Modeling techniques
• OOM (Object Oriented Modeling)
– More natural to Objects/features/behaviors
– Flexible relationships
– Various aspect models
• Structural
• Behavioral
• Sequences
• Timing
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27. MetadataStructureMetadata PackageMetadata Attribute
MetadataMetadata Attribute
Value
Mapping Info
Dataset Version
Dataset
Metadata Compound
Attribute
Metadata Simple
Atribute
Metdata Package
Usage
Data Container
Metdata Attribute
Usage
Metdata Compound
Usage
Base Usage
1
+Parent
+Children
11
11..*
1
10..*
1
{No Extended Property}
2..*
1..*
Modeling Techniques
Structural Aspect
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28. Why to do modeling?
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29. Benefits: Communication
• Various stakeholders
– Domain experts
– Principal Investigators
– Developers
– Managers
• Visual
• Formal (no/very low interpretation possibility)
• Contracting/ Outsourcing
• Standardization (if well-modeled and comprehensive)
• Publishing
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30. Benefits: System Generation
• System Specification
• Automatic Database Generation
• Model Driven Development (MDD)
• Reproducibility
• Cost reduction
• Multi platform targeting
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31. Benefits: Project Management
• Work Breakdown
• Cost/Effort Estimation
• Sub contracting/Outsourcing
• Monitoring
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32. Benefits: Ontology
• OOMs can be transformed to Ontologies
• To provide:
– Formal
– Machine enforceable
– Domain specific
– Semantically annotated
– Geosciences Data
• Improves cross project/ cross domain
– Data integration
– Data Discovery
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33. Benefits: Data Validation
• Model items as rules
• Domain specific constraints
can be incorporated
• Automatic Data Validation
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34. Case Study: BExIS
• BExIS
– A Generic Data Management System
– Complex Conceptual Model
– Multiple Teams work on different parts
– Automatic database generation
– Conceptual Model <-> Ontology
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35. Case Study: BExIS
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36. Some resources used
• BGS Rock Classification Scheme,
see: https://www.bgs.ac.uk/bgsrcs/
• NADM Conceptual Model 1.0—A conceptual
model for geologic map information:
http://pubs.usgs.gov/of/2004/1334
• Semantic Web for Earth and Environmental
Terminology (SWEET): http://sweet.jpl.nasa.gov/
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37. Related Work
• A conceptual model for data management in the field
of ecology, J. Chamanara, B. König-Ries, 2013
• An Extensible Conceptual Model for Tabular Scientific
Datasets, J. Chamanara, M. Owonibi, A. Algergawy, R.
Gerlach
• T. Kiani, 2010, Modeling for geospatial database:
Application to structural geology data. Dissertation,
Pierre and Marie Curie University, 295 p.
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38. Online Resources
• The BExIS complete conceptual model:
http://fusion.cs.uni-jena.de/bppCM/index.htm
• A public talk on the BExIS conceptual model:
http://www.db-thueringen.de/servlets/DocumentServlet?id=27235
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39. Feedback
Thank YOU
Sources of the examples/photos are in the slide
notes
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