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Added Value of Conceptual Modeling in
Geosciences
Tayebeh Kiani, Javad Chamanara
February 2016
Tehran, Iran
Earth
2Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
• Earth is a complex system
Geosciences
3Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
• The science of Earth is complicated…
Hence, the data!
Data in Geosciences
• Data in Geoscience is VERY
– Big
– Diverse
– Complex
– Volatile
– Inter-connected
• Look at
– EPA
– USGS
– OneGeology
– GEON
– EarthCube
4Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Paradigm Shift
• From:
– Experimental
– Theoretical
– Computational
• Data Intensive Science has emerged!
– Doing science by analyzing data
5Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Modeling
• A representation of:
– Process
– Concept
– Operation
of a System
6Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Modeling
• Representation often implies
– Simplification
– Easy Understanding
– Easy Validation
7Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Data Model
• Representation of a system in term of:
– Entities
– Relationships
– Data Flows
– Workflows
Analogous to Geographic maps
8Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Data Modeling
• The process of creating a data model
• For an information system
• By applying formal techniques
• Using proper tools (usually)
Analogous to Cartography
9Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Data Modeling in Geoscience
“A rock is a naturally occurring
solid aggregate of one or more
minerals or mineraloids”– Wikipedia
10Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
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?
Data Modeling in Geosciences
• British Geological Survey
– Open Geological Data Models
• Geochemistry Data Model
11Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
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
• NADM Conceptual Model 1.0
• Geologic concept hierarchy
The Geologic Map of NADM
12Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
GeoSciML Australia
13Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
GSI Iran
• I know of a lot work done
– Unclear licensing!
– Not published!!
• So…
– Not Accessible!
14Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Applications
• Organizational Information Architecture
15Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Applications
• Information System Development
16Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Applications• Communication Medium
17Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Modeling Aspects
• Structural Modeling
objects, their classifiers,
relationships, attributes and
operations
18Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Modeling Aspects
• Behavioral Modeling:
Anything that changes
the objects, events,
sequences or
operations, and object
interactions
19Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Modeling Aspects
• Flow Modeling
20Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Modeling Approaches
21Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Network/Graph Data Model
• Water Grid Modeling
• Process Modeling
Modeling Approaches
22Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
• RDF
• Graph Databases
• Neo4J
• IBM System G
• Info Grid
Modeling Approaches
23Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
USGS Lithology
Relational Data Model
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
24Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Modeling Techniques
• ERDs:
– Are mostly relational
– Do not capture behaviors
– Do not capture processes and sequences
25Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Modeling techniques
• OOM (Object Oriented Modeling)
– More natural to Objects/features/behaviors
– Flexible relationships
– Various aspect models
• Structural
• Behavioral
• Sequences
• Timing
26Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
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
27Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Why to do modeling?
28Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
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
29Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Benefits: System Generation
• System Specification
• Automatic Database Generation
• Model Driven Development (MDD)
• Reproducibility
• Cost reduction
• Multi platform targeting
30Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Benefits: Project Management
• Work Breakdown
• Cost/Effort Estimation
• Sub contracting/Outsourcing
• Monitoring
31Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
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
32Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Benefits: Data Validation
• Model items as rules
• Domain specific constraints
can be incorporated
• Automatic Data Validation
33Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Case Study: BExIS
• BExIS
– A Generic Data Management System
– Complex Conceptual Model
– Multiple Teams work on different parts
– Automatic database generation
– Conceptual Model <-> Ontology
34Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Case Study: BExIS
35Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
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/
36Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
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.
37Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
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
38Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
Feedback
Thank YOU
Sources of the examples/photos are in the slide
notes
39Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran

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Added Value of Conceptual Modeling in Geosciences

  • 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 3Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran • 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 4Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 5. Paradigm Shift • From: – Experimental – Theoretical – Computational • Data Intensive Science has emerged! – Doing science by analyzing data 5Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 6. Modeling • A representation of: – Process – Concept – Operation of a System 6Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 7. Modeling • Representation often implies – Simplification – Easy Understanding – Easy Validation 7Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 8. Data Model • Representation of a system in term of: – Entities – Relationships – Data Flows – Workflows Analogous to Geographic maps 8Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 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 9Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 10. Data Modeling in Geoscience “A rock is a naturally occurring solid aggregate of one or more minerals or mineraloids”– Wikipedia 10Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran 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 11Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran 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 12Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 13. GeoSciML Australia 13Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 14. GSI Iran • I know of a lot work done – Unclear licensing! – Not published!! • So… – Not Accessible! 14Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 15. Applications • Organizational Information Architecture 15Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 16. Applications • Information System Development 16Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 17. Applications• Communication Medium 17Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 18. Modeling Aspects • Structural Modeling objects, their classifiers, relationships, attributes and operations 18Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 19. Modeling Aspects • Behavioral Modeling: Anything that changes the objects, events, sequences or operations, and object interactions 19Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 20. Modeling Aspects • Flow Modeling 20Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 21. Modeling Approaches 21Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 22. Network/Graph Data Model • Water Grid Modeling • Process Modeling Modeling Approaches 22Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran • RDF • Graph Databases • Neo4J • IBM System G • Info Grid
  • 23. Modeling Approaches 23Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran 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 24Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 25. Modeling Techniques • ERDs: – Are mostly relational – Do not capture behaviors – Do not capture processes and sequences 25Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 26. Modeling techniques • OOM (Object Oriented Modeling) – More natural to Objects/features/behaviors – Flexible relationships – Various aspect models • Structural • Behavioral • Sequences • Timing 26Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 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 27Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 28. Why to do modeling? 28Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 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 29Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 30. Benefits: System Generation • System Specification • Automatic Database Generation • Model Driven Development (MDD) • Reproducibility • Cost reduction • Multi platform targeting 30Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 31. Benefits: Project Management • Work Breakdown • Cost/Effort Estimation • Sub contracting/Outsourcing • Monitoring 31Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 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 32Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 33. Benefits: Data Validation • Model items as rules • Domain specific constraints can be incorporated • Automatic Data Validation 33Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 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 34Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 35. Case Study: BExIS 35Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 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/ 36Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 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. 37Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 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 38Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran
  • 39. Feedback Thank YOU Sources of the examples/photos are in the slide notes 39Data Modeling in GeoSciences, Feb. 2016, Tehran, Iran

Editor's Notes

  1. A photo of Earth
  2. A photo of a data scientist and a huge amount of data, but without a hammer! The scientist sees the world through data
  3. Geosciences Network (GEON) EPA: Environmental Protection Agency USGS: US Geological Survey
  4. A data model is a set of symbols and text used for communicating a precise representation of an information landscape. Data entities are determined by the requirements.
  5. Its purpose, scope, methods, and tools are set
  6. http://www.bgs.ac.uk/services/dataModels/geochemistry.html Basic, foundation of other works
  7. http://pubs.usgs.gov/of/2004/1334/2004-1334.pdf
  8. http://www.geosciml.org/
  9. http://www.uhasselt.be/images/faculteiten/bew/mis.jpeg
  10. http://geography.about.com/od/physicalgeography/a/Plate-Tectonics.htm
  11. Natural processes and events are translated to data and workflows
  12. Hierarchical data models http://www.geol.umd.edu/~jmerck/GEOL388/images/02/granite.jpg
  13. https://labs.vmware.com/wp-content/uploads/2013/06/VMW-DGRM-CRITICALITY-PATTERNS-IN-DIRECTED-GRAPH-102.png
  14. http://pubs.usgs.gov/of/2001/of01-223/freeman1.gif
  15. This slide is created for fun. Stop at each item.
  16. It is necessary to model first!
  17. It is necessary to model first!
  18. The actual implementation can be specified at generation time. The model can be implemented on different platforms
  19. A WBS, a handshake
  20. An ontology is an explicit specification of a conceptualization Ontology-driven conceptual modeling Basin ontology Reservoir ontology Task ontology World Oil and Gas Atlas ontology SWEET Unit or Measurement/ Geographical Time Unit/ Conversion
  21. Rational Software Architect, IBM