context observation measurement relationship entity characteristic value standard...

Context

Observation

Measurement

Relationship

Entity

CharacteristicValue

Standard

hasContextRelationship

ofEntity

hasValue ofCharacteristic

usesStandard

hasMeasurement

hasContext

hasContextObservation

0..*

1..1

1..10..*

0..* 1..1

0..* 1..11..1 0..*

0..*

1..1

1..1

0..*

Annotation Examples (12/18/2009)

OBOE Conceptual Model

Annotation Examples (12/18/2009)<observation label="o1”> <entity id=”TemporalRange"/> <measurement label="m1”> <characteristic id=”Year"/> <standard id=”DateTime"/> </measurement></observation><observation label="o2"> <entity id=“Tree"/> <measurement label="m2" precision="0.1"> <characteristic id=”DBH"/> <standard id=”Centimeter"/> </measurement> <measurement label="m3"> <characteristic id=”TaxonomicTypeName"/> <standard id=”ITIS"/> </measurement> <measurement label="m4”> <characteristic id=”EntityName"/> <standard id=“LocalTreeNames"/> </measurement> <context observation="o1"> <relationship id=“Within"/> </context></observation><map attribute="yr" measurement="m1"/><map attribute="diam" measurement="m2" if="diam ge 0"/><map attribute="spec" measurement="m3"/><map attribute="spp" measurement="m4" value="Picea rubens” if="spp eq 'piru'"/><map attribute="spp" measurement="m4" value="Abies balsamea” if="spp eq 'abba'"/>

Annotation Syntax

observation "o1” entity ”TemporalRange” measurement "m1” characteristic ”Year” standard ”DateTime”observation "o2” entity “Tree” measurement "m2" precision: "0.1” characteristic “DBH” standard ”Centimeter” measurement "m3” characteristic “TaxonomicTypeName” standard “ITIS” measurement "m4” characteristic “EntityName” standard “LocalTreeNames” context observation “o1” relationship “Within”map “yr" to “m1”map “diam” to “m2" if diam > 0map “spec" to “m3”map “spp" to “m4" if spp == “piru” value=“Picea rubens” map “spp" to “m4" if spp == “abba” value=“Abies balsamea”

* Code exists to read/write annotations using this XML format


AnnotationDataset

Materialize

Define

(view def.)

OBOE Model(individuals/triples)

OBOE Concepts

instantiates

uses terms from

observation-basedrepresentation of

Query*

* Conceptually, we want to query datasets via annotations


yr spec spp dbh

2007 1 piru 35.8

2008 1 piru 36.2

2008 2 abba 33.2

observation "o1” entity ”TemporalRange” measurement "m1” characteristic ”Year” standard ”DateTime”observation "o2” entity “Tree” measurement "m2" precision: "0.1” characteristic “DBH” standard ”Centimeter” measurement "m3” characteristic “TaxonomicTypeName” standard “ITIS” measurement "m4” characteristic “EntityName” standard “LocalTreeNames” context observation “o1” relationship “Within”map “yr" to “m1”map “dbh” to “m2" if dbh > 0map “spec" to “m3”map “spp" to “m4" if spp == “piru” value=“Picea rubens” map “spp" to “m4" if spp == “abba” value=“Abies balsamea”

Annotation Dataset

* Basic idea: go row-by-row through dataset, generating individuals/triples

: Obs

: Meas

: Year

: DateTime

2007

: Obs

: Meas

: EntN

: LocTN.

1

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

35.8

: Obs

: Meas

: Year

: DateTime

2008

: Obs

: Meas

: EntN

: LocTN.

1

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

36.2

: Obs

: Meas

: Year

: DateTime

2008

: Obs

: Meas

: EntN

: LocTN.

2

: Meas

: TaxN

: ITIS

Abie.

: Meas

: DBH

: Centim.

33.2

: Tree: Tempral

Range

: Tree: Tempral

Range

: Tree: Tempral

Range

hasContext

hasContext

hasContext


yr spec spp dbh

2007 1 piru 35.8

2008 1 piru 36.2

2008 2 abba 33.2

observation "o1” entity ”TemporalRange” measurement "m1” characteristic ”Year” standard ”DateTime”observation "o2” entity “Tree” measurement "m2" precision: "0.1” characteristic “DBH” standard ”Centimeter” measurement "m3” characteristic “TaxonomicTypeName” standard “ITIS” measurement "m4” characteristic “EntityName” standard “LocalTreeNames” context observation “o1” relationship “Within”map “yr" to “m1”map “dbh” to “m2" if dbh > 0map “spec" to “m3”map “spp" to “m4" if spp == “piru” value=“Picea rubens” map “spp" to “m4" if spp == “abba” value=“Abies balsamea”

Annotation Dataset

• Same Trees!! (both have name = 1)• Same Year and year observation!!

: Obs

: Meas

: Year

: DateTime

2007

: Obs

: Meas

: EntN

: LocTN.

1

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

35.8

: Obs

: Meas

: Year

: DateTime

2008

: Obs

: Meas

: EntN

: LocTN.

1

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

36.2

: Obs

: Meas

: Year

: DateTime

2008

: Obs

: Meas

: EntN

: LocTN.

2

: Meas

: TaxN

: ITIS

Abie.

: Meas

: DBH

: Centim.

33.2

: Tree: Tempral

Range

: Tree: Tempral

Range

: Tree: Tempral

RangehasContext

hasContext

hasContext


yr spec spp dbh

2007 1 piru 35.8

2008 1 piru 36.2

2008 2 abba 33.2

observation "o1” distinct yes entity ”TemporalRange” measurement "m1” key yes characteristic ”Year” standard ”DateTime”observation "o2” entity “Tree” measurement "m2" precision: "0.1” characteristic “DBH” standard ”Centimeter” measurement "m3” characteristic “TaxonomicTypeName” standard “ITIS” measurement "m4” key yes characteristic “EntityName” standard “LocalTreeNames” context observation “o1” relationship “Within”map “yr" to “m1”map “dbh” to “m2" if dbh > 0map “spec" to “m3”map “spp" to “m4" if spp == “piru” value=“Picea rubens” map “spp" to “m4" if spp == “abba” value=“Abies balsamea”

Annotation Dataset

: Obs

: Meas

: Year

: DateTime

2007

: Obs

: Meas

: EntN

: LocTN.

1

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

35.8

: Obs

: Meas

: EntN

: LocTN.

1

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

36.2

: Obs

: Meas

: Year

: DateTime

2008

: Obs

: Meas

: EntN

: LocTN.

2

: Meas

: TaxN

: ITIS

Abie.

: Meas

: DBH

: Centim.

33.2

: Tree: Tempral

Range

: Tree: Tempral

RangeEvery observation has an implicit “distinct” attribute (set to “no”)… and every measurement has an implicit “key” attribute (set to “no”)

hasContext

hasContext

• Observation measurement keys– Like a primary key constraint

– States that observation instances with the same measurement key values are of the same entity instance

– Does not imply the same observation instance, unless the observation is declared distinct

– All key measurements of an observation together form the primary key

• Distinct observations – Only applies if at least one key measurement is defined

– States that observation instances with the same entity instance are the same observation instance



plt spp dbh

A piru 35.8

A piru 36.2

B piru 33.2

observation "o1” distinct yes entity ”Plot” measurement "m1” key yes characteristic ”EntityName” standard ”Nominal”observation "o2” entity “Tree” measurement "m2" precision: "0.1” characteristic “DBH” standard ”Centimeter” measurement "m3” key yes characteristic “TaxonomicTypeName” standard “ITIS” context observation “o2” relationship “Within”map “plt" to “m2”map “dbh” to “m3”map “spp" to “m4" if spp == “piru” value=“Picea rubens” map “spp" to “m4" if spp == “abba” value=“Abies balsamea”

Annotation Dataset

: Obs

: Meas

: EntN

: Nominal

A

: Obs

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

35.8

: Obs

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

36.2

: Obs

: Meas

: EntN

: Nominal

B

: Obs

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

33.2

: Tree: Plot

: Plot

hasContext

hasContext

Here we don’t have unique ids for treesBut, assume each spp name within a plot uniquely identifies a tree … i.e., at most one tree of a particular type was measured (possibly multiple times) in each plot


plt spp dbh

A piru 35.8

A piru 36.2

B piru 33.2

observation "o1” distinct yes entity ”Plot” measurement "m1” key yes characteristic ”EntityName” standard ”Nominal”observation "o2” entity “Tree” measurement "m2" precision: "0.1” characteristic “DBH” standard ”Centimeter” measurement "m3” key yes characteristic “TaxonomicTypeName” standard “ITIS” context observation “o2” relationship “Within”map “plt" to “m2”map “dbh” to “m3”map “spp" to “m4" if spp == “piru” value=“Picea rubens” map “spp" to “m4" if spp == “abba” value=“Abies balsamea”

Annotation Dataset

: Obs

: Meas

: EntN

: Nominal

A

: Obs

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

35.8

: Obs

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

36.2

: Obs

: Meas

: EntN

: Nominal

B

: Obs

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

33.2

: Tree: Plot

: Plot

hasContext

hasContext

• The Tree entity instance should depend on the plot it is in!!! (context)


plt spp dbh

A piru 35.8

A piru 36.2

B piru 33.2

observation "o1” distinct yes entity ”Plot” measurement "m1” key yes characteristic ”EntityName” standard ”Nominal”observation "o2” entity “Tree” measurement "m2" precision: "0.1” characteristic “DBH” standard ”Centimeter” measurement "m3” key yes characteristic “TaxonomicTypeName” standard “ITIS” context identifying yes observation “o2” relationship “Within”map “plt" to “m2”map “dbh” to “m3”map “spp" to “m4" if spp == “piru” value=“Picea rubens” map “spp" to “m4" if spp == “abba” value=“Abies balsamea”

Annotation Dataset

: Obs

: Meas

: EntN

: Nominal

A

: Obs

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

35.8

: Obs

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

36.2

: Obs

: Meas

: EntN

: Nominal

B

: Obs

: Meas

: TaxN

: ITIS

Picea.

: Meas

: DBH

: Centim.

33.2

: Tree: Plot

: Plot

hasContext

hasContext

Every context relationship has an “identifying” qualifier (set to “no”)Uniqueness within context observationSimilar to a weak-entity constraint (ER)

: Tree

Representing instances …

• Annotation(AnnotId, Resource)

• Observation(ObsId, AnnotId, EntId)

• Measurement(MeasId, ObsId, MeasType, Value)

• Context(ObsId1, ObsId2, Rel)

• Relationship(RelId, RelType)

• Entity(EntId, EntType)

This could be queried itself and/or mapped to triples

Note that ObsIds are unique across annotationsContext.ObsId’s must be for the same annotation


* Simple relational schema for OBOE models (individuals/triples)

Representing annotations …

• Annotation(AnnotId, Res)

• ObservationType(ObsTypeId, AnnotId, EntType, Unique)

• MeasType(MeasTypeId, ObsTypeId, CharType, StdType, ProtType, Precision, Value, Key)

• ContextType(ObsTypeId1, ObsTypeId2, RelType)

• Map(ResAttribute, MeasType, Condition, Value)


Materialization Algorithm

• Start with simple case of no key, unique, and identifying constraints

• Add these incrementally

• Define algorithm so that it works one row at a time

• Can we also define the algorithm as a view, to enable querying through views (rewriting)?

– This was what the prolog code did …


MapRow(Row : Dom(A1)×Dom(A2) Dom(⋯ An), AnnotId : int)

let D = [] /* D is a dictionary (ObsTypeId, Keys) → ObsId */

foreach ⟨Ai, MeasTypedId, Cond, Val ⟩ in Map where satisfies(Row, Ai, Cond)

select ⟨MeasTypeId, ObsTypeId, Std, Key ⟩ from MeasType for MeasTypeId

let MeasId = CreateNewId()

let Keys = GetObsTypeKeys(ObsTypeId, Row, AnnotId)

let ObsId = CreateObsId(ObsTypeId, Keys, D, Row, AnnotId)

D = D [(∪ ObsTypeId, Keys) → ObsId]

let AiVal = GetValue(Row[Ai], Cond, Val)

insert ⟨MeasId, ObsId, Std, AiVal ⟩ into Meas

end MapRow


context observation measurement relationship entity characteristic value standard...

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