Managing different views of data
Simon Cox
CSIRO Exploration and Mining
29 November 2006
www.csiro.au
Outline
OGC/ISO meta-models for information objects
 Features and coverages
Property estimation events
 Observations
Transforming viewpoints
Observations, Features and Coverages
2 of 24
Conceptual object model: features
Digital objects correspond with
identifiable, typed,
objects in the real world
 mountain, road, specimen, event,
tract, catchment, wetland, farm, bore,
reach, property, license-area, station
Feature-type is characterised by a
specific set of properties
Specimen
 ID (name)
 description
 mass
 processing details
 sampling location
 sampling time
 related observation
 material
 …
Observations, Features and Coverages
3 of 24
ISO 19101, 19109 General Feature Model
Properties include
 attributes
 associations between objects
 value may be
object with identity
 operations
Metaclass diagram
Observations, Features and Coverages
4 of 24
Geology domain model - feature type catalogue
Borehole
 collar location
Conceptual classification
 shape
Multiple geometries
 collar diameter
Fault
 length
 shape
 operator
 surface trace
 logs
License
 displacement
 related area
observations
 age
 …  issuer
Ore-body
 …
 holder
 commodity
 interestedParty
 deposit type
 shape(t)
 host formation
Geologic Unit
 right(t)
 shape
 classification
 …
 resource estimate
 shape
 …
 sampling frame
 age
 dominant lithology
 …
Observations, Features and Coverages
5 of 24
Water resources feature type catalogue
Aquifer
Storage
Stream
Well
Entitlement
Observation
…
Observations, Features and Coverages
6 of 24
Meteorology feature type catalogue
Front
Jetstream
Tropical cyclone
Lightning strike
Pressure field
Rainfall distribution
…
Bottom two are a different kind of feature
Observations, Features and Coverages
7 of 24
Spatial function: coverage
Variation of a property across the domain of interest
(x1,y1)
(x2,y2)
 For each element in a spatio-temporal domain, a value from the
range can be determined
 Used to analyse patterns and anomalies, i.e. to detect features
(e.g. storms, fronts, jetstreams)
Discrete or continuous domain
 Domain is often a grid
 Time-series are coverages over time
Observations, Features and Coverages
8 of 24
ISO 19123 Coverage model
class Fig 03 - CV_Cov erage subclasses
«Abstract»
CV_Coverage
{n}
+
+
+
commonPointRule: CV_CommonPointRule
domainExtent[1..*]: EX_Extent
rangeType: RecordType
+
+
+
+
+
evaluate(DirectPosition, Sequence<CharacterString>) : Record
evaluateInverse(Record) : Set<CV_DomainObject>
find(DirectPosition, Integer) : Sequence<CV_GeometryValuePair>
list() : Set<CV_GeometryValuePair>
select(GM_Object, TM_Period) : Set<CV_GeometryValuePair>
«Abstract»
CV_ContinuousCoverage
Discrete Cov erages::CV_DiscreteCov erage
{n}
{n}
+
locate(DirectPosition) : Set<CV_GeometryValuePair>
+collection
0..*
+
+
interpolationParametersType[0..1]: Record
interpolationType: CV_InterpolationMethod
+
+
locate(DirectPosition) : Set<CV_ValueObject>
locateRegion(GM_Object) : Set<CV_ValueObject>
CoverageFunction
+collection
0..*
«CodeList»
CV_InterpolationMethod
{n}
CoverageFunction
+element
+element
1..*
«Abstract»
CV_ValueObject
CV_GeometryValuePair
{n} +controlValue
+
+
1..*
geometry: CV_DomainObject
value: Record
Observations, Features and Coverages
1..*
Control
{n}
+extension
0..*
+
+
geometry: CV_DomainObject
interpolationParameters[0..1]: Record
+
interpolate(DirectPosition) : Record
+
+
+
+
+
+
+
+
+
barycentric:
bicubic:
bilinear:
biquadratic:
cubic:
linear:
lostarea:
nearestneighbor:
quadratic:
9 of 24
Discrete coverage model
CV_Coverage
CV_DiscreteCov erage
+collection
0..*
CV_DiscretePointCov erage
CV_DiscreteTimeInstantCov erage
+element 1..*
«DataType»
CV_GeometryValuePair
+
+
0..*
+collection
0..*
geometry: CV_DomainObject
value: Record
+element
«DataType»
CV_PointValuePair
+
+collection
geometry: GM_Point
1..*
+element 1..*
«DataType»
CV_TimeInstantValuePair
+
Observations, Features and Coverages
geometry: TM_Instant
10 of 24
Features vs Coverages
Feature
 object-centric
 heterogeneous collection of properties
 “summary-view”
Coverage
 property-centric
 variation of homogeneous property
 patterns & anomalies
Both needed; transformations required
Observations, Features and Coverages
11 of 24
“Cross-sections” through collections
Specimen
Au (ppm)
Cu-a (%)
Cu-b (%)
As (ppm)
Sb (ppm)
ABC-123
1.23
3.45
4.23
0.5
0.34
A Row gives properties of
one feature
A Column = variation of a single property
across a domain (i.e. set of locations)
Observations, Features and Coverages
12 of 24
Assignment of property values
For each property of a feature, the value is either
i. asserted
 name, owner, price, boundary (cadastral feature types)
ii. estimated
 colour, mass, shape (natural feature types)
 i.e. error in the value is of interest
Observations, Features and Coverages
13 of 24
Value estimation process: observation
An Observation is a kind of “Event Feature type”,
whose result is a value estimate,
and whose other properties provide metadata concerning
the estimation process
Observations, Features and Coverages
14 of 24
Observation model – Value-capture-centric view
+precedingEvent 0..*
«FeatureType»
Event
«Union»
Procedure
+
+
+followingEvent 0..*
+
+
procedureType: ProcedureSystem
procedureUse: ProcedureEvent
+procedure
eventParameter: TypedValue [0..*]
time: TM_Object
«DataType»
TypedValue
+
+
property: ScopedName
value: Any
1
AnyDefinition
+generatedObservation
0..*
+
+
+
0..*
1
AnyIdentifiableObject
«FeatureType»
Observ ation
quality: DQ_Element [0..1]
responsible: CI_ResponsibleParty [0..1]
result: Any
«ObjectType»
Phenomenon
+observedProperty
1
{Definition must be of a
phenomenon that is a property
of the featureOfInterest}
+propertyValueProvider
+featureOfInterest
«FeatureType»
AnyIdentifiableFeature
An Observation is an Event whose result is an estimate of the value
of some Property of the Feature-of-interest, obtained using a specified Procedure
Observations, Features and Coverages
15 of 24
“Cross-sections” through collections
A Cell describes the value of a single property on a feature,
often obtained by observation or measurement
Specimen
Au (ppm)
Cu-a (%)
Cu-b (%)
As (ppm)
Sb (ppm)
ABC-123
1.23
3.45
4.23
0.5
0.34
A Row gives properties of
one feature
A Column = variation of a single property
across a domain (i.e. set of features)
Observations, Features and Coverages
16 of 24
Feature of interest
may be any feature type from any domain-model …
observations provide values for properties whose values are not
asserted
i.e. the application-domain supplies the feature types
Observations, Features and Coverages
17 of 24
Observations support property assignment
These must match if the observation is coherent with the feature property
+observedProperty
Mass :
Phenomenon
Observation
Measurement
+
result: RelativeMeasure
+featureOfInterest
SamplingFeature
SamplingFeature
+propertyValueProvider
Specimen
Specimen
currentLocation: Location
Location [0..1]
[0..1]
++ currentLocation:
mass: Measure
Measure
++ mass:
material: CV_Coverage
CV_Coverage
++ material:
+observedProperty
Material :
Phenomenon
+propertyValueProvider
Observation
+featureOfInterest
Cov erageObserv ation
+
result: CV_DiscreteCoverage
Some properties have interesting types …
Observations, Features and Coverages
18 of 24
Variable property values
Some property values are not constant
 colour of a Scene or Swath varies with position
 shape of a Glacier varies with time
 temperature at a Station varies with time
 rock density varies along a Borehole
Variable values may be described as a Coverage over some
axis of the feature
Observations, Features and Coverages
19 of 24
Observations and coverages
If the property value is not constant across the feature-ofinterest
 varies by location, in time
the corresponding observation result is a coverage
individual samples must be tied to the location within the
domain, so result is set of e.g.
 time-value
 position-value
 (stationID-value ?)
Time-series observations are a particularly common use-case
Observations, Features and Coverages
20 of 24
Observations, features and coverages
Same property on
multiple samples
A property-value
2580 kg/T : +result
is a another kind
may be a coverage
Measure
of coverage
Material :
Phenomenon
DensityItB :
+observedProperty
Observ ation
+time
2005-12-23 :
+procedure
TM_Instant
+density
+featureOfInterest
Multiple observations
+result
MineralDistribution
+samplingLocation
RockSample-B
:
:CV_Cov
erage
one feature,West Leederv ille,
Specimen
WA :Location
different properties:
+material
feature summary evidence
ProbeItA :
Observ ation
Multiple observations
different features,
one property:
coverage evidence
Microprobe :
Observ ationProcedure
+time 2006-11-24/2006-11-26 :
TM_Period
+observedProperty
+procedure
Density :
Phenomenon
Densitometry :
Observ ationProcedure
+featureOfInterest
Feature
summary
Leederv ille, WA : +samplingLocation
Location
RockSample-A :
Specimen
+observedProperty
+procedure
Property-value
evidence
+featureOfInterest
+density
2610 kg/T : +result
Measure
Observations, Features and Coverages
DensityItA :
Observ ation
+time
2006-11-23 :
TM_Instant
21 of 24
Features, Coverages & Observations (1)
Observations and Features
 An observation provides evidence for estimation of a property value
for the feature-of-interest
Features and Coverages (1)
 The value of a property that varies on a feature
defines a coverage whose domain is the feature
Observations and Coverages (1)
 An observation of a property sampled at different times/positions on
a feature-of-interest estimates a discrete coverage whose domain is
the feature-of-interest
 feature-of-interest is one big feature – property value varies within it
Observations, Features and Coverages
22 of 24
Features, Coverages & Observations (2)
Observations and Features
 An observation provides evidence for estimation of a property value
for the feature-of-interest
Features and Coverages (2)
 The values of the same property from a set of features constitutes a
discrete coverage over a domain defined by the set of features
Observations and Coverages (2)
 A set of observations of the same property on different features
provides an estimate of the range-values of a discrete coverage
whose domain is defined by the set of features-of-interest
 feature-of-interest is lots of little features – property value constant
on each one
Observations, Features and Coverages
23 of 24
Conclusions
Feature and coverage viewpoints used for different purposes
 Summary vs. analysis
Some values are determined by observation
 Sometimes the description of the estimation process is necessary
Transformation between feature and coverage views depends on the
“feature-type”
Management of observation evidence depends on feature-ofinterest-type
 One big feature, with internal variation,
vs
 Aggregation of many small features
Observations, Features and Coverages
24 of 24
CSIRO Exploration and Mining
Name
Simon Cox
Title
Research Scientist
Phone
+61 8 6436 8639
Email
[email protected]
Web
www.seegrid.csiro.au
Thank You
Contact CSIRO
Phone
1300 363 400
+61 3 9545 2176
Email
[email protected]
Web
www.csiro.au
www.csiro.au
Sensor service
premises:
O&M is the high-level information model
SOS is the primary information-access interface
SOS can serve:
an Observation (Feature)
 getObservation == “getFeature” (WFS/Obs) operation
a feature of interest (Feature)
 getFeatureOfInterest == getFeature (WFS) operation
or Observation/result (often a time-series == discrete Coverage)
 getResult == “getCoverage” (WCS) operation
or Sensor == Observation/procedure (SensorML document)
 describeSensor == “getFeature” (WFS) or “getRecord” (CSW) operation
Observations, Features and Coverages
optional – probably required for
dynamic sensor use-cases
26 of 24
SOS vs WFS, WCS, CS/W?
getFeature,
type=Observation
WFS/
Obs
getObservation
getCoverage
getResult
describeSensor
getCoverage
(result)
WCS
SOS
getFeatureOfInterest
getRecord
SOS interface is effectively a composition of
(specialised) WFS+WCS+CS/W operations
Sensor
Registry
getFeature
WFS
e.g. SOS::getResult == “convenience” interface for WCS
Observations, Features and Coverages
27 of 24
Some feature types only exist to support observations
Surv eyProcedure
+surveyDetails
+member 0..*
0..1
SamplingFeature
+
SamplingFeatureCollection
responsible: CI_ResponsibleParty [0..1]
Specimen
+
+
+
constraints
{count(member)>=1}
Station
+
+
Profile
elevation: DirectPosition [0..1]
position: GM_Point
+
+
+
SurfaceOfInterest
begin: GM_Point
end: GM_Point
length: RelativeMeasure [0..1]
Interv al
Trav erse
+
currentLocation: Location [0..1]
mass: Measure
material: CV_Coverage
SolidOfInterest
area: RelativeMeasure [0..1]
Sw ath
+
volume: RelativeMeasure [0..1]
LidarCloud
Sounding
Flightline
Section
+shape
Shape1D
Observations, Features and Coverages
1
+shape
Shape2D
1
+shape
1
Shape3D
28 of 24
Observation model
+precedingEvent 0..*
«FeatureType»
Event
«Union»
Procedure
+
+
+followingEvent 0..*
+
+
procedureType: ProcedureSystem
procedureUse: ProcedureEvent
+procedure
eventParameter: TypedValue [0..*]
time: TM_Object
«DataType»
TypedValue
+
+
property: ScopedName
value: Any
1
AnyDefinition
«ObjectType»
Phenomenon
+generatedObservation
0..*
+
+
+
0..*
1
AnyIdentifiableObject
«FeatureType»
Observ ation
quality: DQ_Element [0..1]
responsible: CI_ResponsibleParty [0..1]
result: Any
+observedProperty
1
{Definition must be of a
phenomenon that is a property
of the featureOfInterest}
+propertyValueProvider
+featureOfInterest
«FeatureType»
AnyIdentifiableFeature
Generic Observation has dynamically typed result
Observations, Features and Coverages
29 of 24
Observation specializations
Override result type
Observations, Features and Coverages
30 of 24
Observation specializations
Override result type
Primary use-case for “CommonObservation” matches “CoverageObservation”
 N.B. CommonObservation is an implementation
Observations, Features and Coverages
31 of 24
Observations and Features
An estimated value is determined through observation
i.e. by application of an observation procedure
Observations, Features and Coverages
32 of 24
Invariant property values: cross-sections through collections
A Cell describes the value of a single property on a feature,
often obtained by observation or measurement
Specimen
Au (ppm)
Cu-a (%)
Cu-b (%)
As (ppm)
Sb (ppm)
ABC-123
1.23
3.45
4.23
0.5
0.34
A Row gives properties of
one feature
A Column = variation of a single property
across a domain (i.e. set of features)
Observations, Features and Coverages
33 of 24
Variable property values
Each property value is either
 constant on the feature instance
 e.g. name, identifier
 non-constant
 colour of a Scene or Swath varies with position
 shape of a Glacier varies with time
 temperature at a Station varies with time
 rock density varies along a Borehole
Variable values may be described as a Coverage over some axis of
the feature
Observations, Features and Coverages
34 of 24
Observations support property assignment
Mass :
Phenomenon
+observedProperty
Observation
Measurement
+
+procedure
Scales :
Observ ationProcedure
result: RelativeMeasure
+featureOfInterest
SamplingFeature
SamplingFeature
+propertyValueProvider
Specimen
Specimen
++ currentLocation:
currentLocation: Location
Location [0..1]
[0..1]
++ mass:
mass: Measure
Measure
++ material:
material: CV_Coverage
CV_Coverage
+observedProperty
+propertyValueProvider
Observation
+featureOfInterest
Cov erageObserv ation
+
Observations, Features and Coverages
Material :
Phenomenon
result: CV_DiscreteCoverage
+procedure
MicroProbe :
Observ ationProcedure
35 of 24