semantic mediation, ontologies and scientific workflows and all the rest (+/– web services)

Semantic Mediation, Semantic Mediation, Ontologies and Ontologies and

Scientific Workflows Scientific Workflows and all the rest (+/– Web Services)and all the rest (+/– Web Services)

Bertram Ludäscher

Knowledge-Based Information Systems LabSan Diego Supercomputer Center

University of California San Diego

http://seek.ecoinformatics.org http://www.geongrid.org

http://www.sdsc.edu/

SDSC/LTER Workshop Feb’2004 2

Outline

• Motivation (SEEK, GEON, ..)

• Ontologies 101

• Semantic Mediation, Data Registration, …

• Application Examples (Stargazing with Kepler…)



Kepler Team, Projects, Sponsors

• Ilkay Altintas SDM • Chad Berkley SEEK • Shawn Bowers SEEK• Jeffrey Grethe BIRN• Christopher H. Brooks Ptolemy II • Zhengang Cheng SDM • Efrat Jaeger GEON • Matt Jones SEEK • Edward A. Lee Ptolemy II • Kai Lin GEON• Ashraf Memon GEON• Bertram Ludaescher BIRN, GEON, SDM, SEEK• Steve Mock NMI• Steve Neuendorffer Ptolemy II • Mladen Vouk SDM • Yang Zhao Ptolemy II • …

Ptolemy IIPtolemy II



SEEK

Science Environment for Ecological Knowledge

• EcoGrid• Uniform interfaces to manage environmental data

• Kepler• Modeling scientific workflows

• Semantic Mediation System• “Smart” data discovery and integration

• Knowledge Representation (SEEK-KR)• Classification and Nomenclature (SEEK-TAXON)• Biodiversity and Ecological Analysis and Modeling (SEEK-BEAM)



SEEK Overview



Building the EcoGrid

AND

LUQ

HBR

NTL

Metacat node

Legacy system

LTER Network (24) Natural History Collections (>> 100)Organization of Biological Field Stations (180)UC Natural Reserve System (36)Partnership for Interdisciplinary Studies of Coastal Oceans (4)Multi-agency Rocky Intertidal Network (60)

SRB node

DiGIR node

VCR

VegBank node

Xanthoria node



Heterogeneous Data integration

• Requires advanced metadata and processing

– Attributes must be semantically typed– Collection protocols must be known– Units and measurement scale must be known– Measurement relationships must be known

• e.g., that ArealDensity=Count/Area



• Label data with semantic types• Label inputs and outputs of analytical components with semantic types

• Use reasoning engines to generate transformation steps– Beware analytical constraints

• Use reasoning engine to discover relevant components

Semantic Mediation

Data Ontology Workflow Components



Ecological ontologies

• What was measured (e.g., biomass)• Type of measurement (e.g., Energy)• Context of measurement (e.g., Psychotria limonensis)• How it was measured (e.g., dry weight)

• SEEK intends to enable community-created ecological ontologies using OWL– Represents a controlled vocabulary for ecological metadata

• More about this in Bertram’s talk



Ontologies 101 (based on a tutorial by Shawn Bowers and CSE291)

• Ontologies basicsOntologies basics

• Ontologies and data management

• Benefits of ontologies

• Constructing ontologies

• Breakout Exercises



What are ontologies?

It depends on who you askWe focus on the data-management view

Generally speaking, an ontology

specifies a theoryspecifies a theory (a modelmodel) by …

definingdefining and relatingrelating …

generic conceptsgeneric concepts representing features of the real or abstract world (a domain of interest)

[Bunge]



Concepts, Symbols, and Things

• Humans use symbols (e.g., words) to communicate

• Words are mapped to things indirectly through concepts that denote (refer to) things

Concept

Ogden, C. K. & Richards, I. A. 1923. "The Meaning of Meaning." 8th Ed. New York, Harcourt, Brace & World, Inc

[Carole Goble, Nigel Shadbolt] [Carole Goble, Nigel Shadbolt]

“Jaguar”




Symbols and concepts are not precise– The same symbol can stand for multiple things– The same thing can have multiple symbols– Concepts are usually not well-defined

Concept


[Carole Goble, Nigel Shadbolt] [Carole Goble, Nigel Shadbolt]

“Jaguar”




An ontology attempts to define and relate specific concepts for certain sets of things via agreed upon symbols

Concept


“Jaguar”




Ontologies are typically created to:

Commit to a definition (a model) of a domain

Explicitly state assumptions concerning the definition

Have a wide scope (be general)

Support exchange and integration of heterogeneous data sources and applications (more on this later…)




Ontologies may be expressed

Informally using natural language (e.g., in philosophy and sometimes biology)

Formally using a mathematical language, e.g., first-order logic

We focus on formal ontologies

To be precise about what the theory proposes




Formal ontologies can vary in detail

Controlled Vocabulary (list of terms)

Simple Thesaurus (synonyms)

Thesaurus (broader/narrower terms)

Classification (class, instance, is-a, maybe part-of)Classification

(value, cardinality constraints)Classification (axioms such as disjoint, union, etc.)Classification

(general logic constraints)











Expressiveness



Class, Instance, and Is-a

Animal

Jaguar

is-a“Every Jaguar is an Animal”x . Jaguar(x) Animal(x)

Set of things (instances)denoted by the class Animal

Set of things (instances)denoted by the class Jaguar



Properties and Cardinality Constraints

Animal

Carnivore

is-a

Jaguar

is-a

eats

A cardinality constraintmight state that carnivores

must eat at least oneat least one Animal

Question: Must Jaguars eat at least one Animal?



Value Restrictions

Animal

Carnivore

is-a

Jaguar

is-a

eats

A value restriction for Jaguar might restrict the eats property

to the specific animals eatenby Jaguars



Value Restrictions

Animal

Carnivore

Jaguar

eats

Marsh Deer

Herbivore

eats

Jaguars restrict the eats relationship to Marsh Deer, …



Value Restrictions

Animal

Carnivore

Jaguar

eats

Marsh Deer

Herbivore

eats

Does anyone see a problem with this choice of representation?



Value Restrictions

Animal

Carnivore

Jaguar

eats

Herbivore

eats

JaguarFood

Marsh Deer

Peccary

These different representations propose the same basicunderlying theory











Expressiveness




An (informal) ontology of wine:

Wines are potable liquids made by wineries within regions and with specific vintages

Wines are characterized by the type of grape they are made with, their color (white, rose, red), their sugar (dry, offdry, or sweet), their body (light, medium, full), and their flavor (delicate, moderate, strong)

Sauvignon Blanc, Merlot, Pinot Noir, and Riesling are types of wines

[OWL Guide] [OWL Guide]



Exercise

With a partner, take 5 minutes and try to define a “formal” ontology for the wine example

– Select two or three classes– Identify some relationships between them– List any constraints (cardinality or value

restrictions) that exist between them




(Philosophy) An ontological theory can answer “ontological” questions

– Is Merlot a potable liquid?– Are there wines made of things other than grapes?– How are Pinot Gris and Pinot Noir related? – Are there white wines that are dry, full, and strong

made in Napa Valley?

We will look at other uses later

[Bunge]



Outline

• Ontologies basics

• Ontologies and data managementOntologies and data management

• Benefits of using ontologies





Ontologies and Data Management

Where do ontologies fit within data management architectures?

There is no specific answer to this question…

However, an ontology is similar to a schema or conceptual model if one exists, but is

– Developed independently of a particular application

– Probably given in a different language– Inherently more general– Usually not a very good schema (weak structure)



Ontologies and Data Management( watch out for Semantic Data Registration later)

Schema Schema Schema Schema

ConceptualModel

ConceptualModel

Ontology

Data

Metadata

DesignArtifact

use concepts from(explicitly or implicitly)



Outline



• Benefits of ontologies Benefits of ontologies





Benefits of ontologies

Ontologies are often developed within a community and are interdisciplinary

Explicitly capture “knowledge” about a domain

– Standard terms (symbols) for metadata values and schema design

– Enables advanced searching techniques (via reasoning)

– Enables exchange and integration




Ontologies for metadata keywords

{cabernet sauvignon, sonoma county, …}

{medium, red, dry, …}

{sonoma county, wine}




Ontologies for metadata keywords

{cabernet sauvignon, sonoma region, …}

{medium, red, dry, …}

{sonoma region, wine}

Find information about dry californiadry california red winesred wines

We use the ontology to “expand” and/or “focus” the query, e.g., that cabernet sauvignon is red and dry; sonoma valley is in california




Dataset(region

characteristics)

Dataset(wines by regions)

AnalysisIntegrateDataset

(wine sales)

What regional characteristicsproduce the best-selling wines?

Integration can be extremely complex due to structural (schema and values)and semantic (ontological) differences

Ontologies can help!




Dataset(region

characteristics)

Dataset(wines by regions)

AnalysisIntegrateDataset

(wine sales)

What regional characteristicsproduce the best-selling wines?

Registering datasets with ontologiesRegistering datasets with ontologies

Map structure (schema) to concepts

Map data to classes/instances

(various ways to do this…)

Provides a uniform view of disparate sources



Outline




• Constructing ontologiesConstructing ontologies




Constructing ontologies

Various Web-based standards are emerging for defining ontologies

XML Schema• Mainly for defining “vocabularies” and less-formal

ontologies (term-based is-a, some constraints)• Mainly a structural/schema representation

– Topic Maps• For advanced thesauri, subject indexes

– RDF/RDFS/OWL• Formal ontologies based on description logics (a variant of

first-order logic) and semantic networks (more informal)



Resource Description Framework (RDF)

Simple data model that consists of– Resources (uniquely identified via URIs)– Properties – Values (resources or character strings)

Data organized into triples (subject, property, value)

SonomaRegion CaliforniaRegionlocatedIn

Subject(Resource)

Value(Resource)

Property(Resource)

locatedIn(SonomaRegion, California)



RDF Schema

Adds a set of pre-defined properties to define classes and properties

Allows instances to be connected to classes

Sub-class and sub-property (is-a) relationships

SonomaRegion CaliforniaRegionlocatedIn

Region

rdf:type rdf:type

locatedInRegion is a classlocatedIn is a propertylocatedIn connects Regions



OWL

Adds additional pre-defined properties to further constrain an ontology(See http://www.w3.org/TR/owl-guide/)

Note, RDF(S) and OWL use XMLSome graphic tools exist (e.g., Protégé)

<owl:Class rdf:ID="Vintage"> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource="#hasVintageYear"/> <owl:cardinality>1</owl:cardinality> </owl:Restriction> </rdfs:subClassOf> </owl:Class>

A Vintage is a class that is a subclass of an unnamed class whose instances always have

one hasVintageYear property.

Note the uglified XML syntax…The good news: meant for

parsers, not humans!



Protégé



Description Logic

A language and syntax for describing “concept” logics

– Concept names C (denote sets of instances)– Class definitions D (denote sets of instances)– Subclass definition C ⊑ D– Equivalence definition C D– Definition constructors

• intersection D ⊓ D• union D ⊔ D• Property existence hasProp.D• Property restriction hasProp.D• Cardinality =1 hasProp.D, >1 hasProp.D, <2 hasProp.D



Description Logic

Wine ⊑ PotableLiquid ⊔ hasColor.{Red, Rose, White)

The class Wine is a sub-class of PotableLiquids that have at least one (exists one) hasColor property whose values are either Red, Rose, or White

WhiteWine Wine ⊓ hasColor.{White)

WhiteWines are exactly Wines whose color is White

WhiteBurgandy ⊑ WhiteWine ⊓ Burgandy

The set of WhiteBurgandy wines is a subset of the set of WhiteWines intersected with Burgandy wines

SauvignonBlanc ⊑ WhiteWine ⊓ =1 madeFromGrape.SauvignonBlancGrape



Constructing Ontologies

In general, creating an ontology is hard

– Requires general agreement and understanding of a domain

– Requires a clear, concise, and unambiguous definition

– May invoke controversy

– Is a hard data-modeling problem (complex constraints, broad domain)



Outline








Breakout Exercises

Divide into the same groups as yesterday

Develop an ontology for the domain you worked on:• Define relevant concepts• Define relationships among concepts• If you have time, work on simple constraints (cardinality, value

restrictions)

Capture (on paper, or in PPT if you feel ambitious) your ontology in whatever way makes sense to you (e.g., as circle-line drawings or as list of terms and properties). What assumptions did you make in creating your ontology?

If you have time, develop a scenario for your ontology in terms of your workflow. For example, to show how your ontology could help integration or query.



Some References

Mario Bunge. Treatise on Basic Philosophy, Vol. 3, Ontology I: The Furniture of the World. D. Reidel Publishing Company, 1977.

Nicola Guarino. Formal ontology and information systems. In Proc. of Formal Ontology in Information Systems, IOS Press, pp. 3-15, 1998.

Thomas R. Gruber. Toward principles for the design of ontologies used for knowledge sharing. In Formal Ontology in Conceptual Analysis and Knowledge Representation, Kluwer Academic Publishers, 1993.

Jeffrey Parsons and Yair Wand. Emancipating instances from the tyranny of classes in information modeling. In ACM Transactions on Database Systems, 25(2):228-268, 2000.



Some References

Michael Smith, Chris Welty, and Deborah McGuinness. OWL Web Ontology Language Guide. W3C Proposed Recommendation. (http://www.w3.org/TR/owl-guide/). Includes Wine Ontology.

Protégé. Stanford Medical Informatics. http://protege.stanford.edu/index.html. Freely available. Lots of plug-ins.


Data Registration


What is Data Registration?

• A mechanism by which data sources are A mechanism by which data sources are published in a repository or registry for the published in a repository or registry for the purpose ofpurpose of– data discovery, querying, retrieval (“get”, data discovery, querying, retrieval (“get”,

“copy”), update, transformation, migration, “copy”), update, transformation, migration, application binding, query planning, concept-application binding, query planning, concept-based rewriting, …based rewriting, …



Things to Register

• Data files (individual files)– e.g. shapefile as a blob (+ file type)

• Collections (of files or subcollections)• Ontologies• Services (web + grid services)• Databases (has schema and can be queried)

– e.g. shapefile as a DB with schema registered – schemas (relational, XML, …), – local integrity constraints, local integrity constraints, – access information (connection mechanism, protocols, access information (connection mechanism, protocols,

query capabilities, handles to actual data) query capabilities, handles to actual data) – registration constraints to (identifiable/registered) registration constraints to (identifiable/registered)

ontologies (aka “registration mappings”)ontologies (aka “registration mappings”)



Things to register (w/ metadata!) aka Registration Objects

• Data files (individual files)– Shapefile as a blob (+ file type)

• Collections (of files; nested; eg satellite data)• Databases (has schema and can be queried)

– Shapefile with schema registered

• Ontologies• Services (web + grid services)• Other/external applications



Connecting Datasets to Ontologies

Date Site Transect SP_Code Count 2000-09-08 CARP 1 CRGI 0 2000-09-08 CARP 4 LOCH 0 2000-09-08 CARP 7 MUCA 1 2000-09-22 NAPL 7 LOCH 1 2000-09-18 NAPL 1 PAPA 5 2000-09-28 BULL 1 CYOS 57


DataCollectionEventMeasurement

MeasurementContextMeasurableItem

SpeciesCountSpeciesAbundance

AbundanceCollectionEventLocation

LTERSiteSBLTERSite

{naples,…}

⊑ contains.Measurement⊑ measureOf.MeasurableItem ⊓ hasContext.MeasurementContext

⊑ hasTime.DateTime ⊓ hasLocation.Location ⊑ hasUnit.Unit ⊓ hasValue.UnitValue ⊑ MeasurableItem ⊓ hasSpecies.Species ⊓ hasUnit.RatioUnit

… ⊑ Measurement ⊓ measureOf.SpeciesCount ⊑ DataCollectionEvent ⊓ contains.SpeciesAbundance ⊑ position.Coordinate ⊑ Location ⊑ LTERSite ⊓ position.SBLTERCoordinate ⊑ SBLTERSite

How can we “register”the dataset to concepts in the Ontology?

Ontology (snippet)

Dataset



Purpose of Semantic Registration

Expose “hidden” information:– What do attributes represent? – What do specific values represent? – What conceptual “objects” are in the dataset?

Capture connections between the dataset and ontology to:– Find existing datasets (or parts of datasets) via

ontological concepts (discovery)– Enable fine-grain integration of datasets

(mediation)– Generate metadata for new data products (in a

pipeline)



Semantic Registration Framework

Step 1: Data provider selects relevant ontological concepts (for the dataset)

Step 2: The semantic registration system creates a structural representation based on chosen concepts (data provide refines if needed)

Step 3: The data provider maps the dataset information to the generated structural representation



Step1: Selecting Relevant Concepts



Concepts from an Ontology

Dataset

• DataCollectionEvent• AbundanceCollectionEvent

• Measurement• Abundance

• SpeciesAbundance

• MeasurableItem• SpeciesCount

• Location• LTERSite

• SBLTERSite• naples

• Species• …

• MeasurementContext• …



Step1: Selecting Relevant Concepts



Concepts from an Ontology

Dataset







• Species• …




Step2: Generate Object ModelConcepts from an Ontology

AbundanceCollection Event

SpeciesAbundance

containsSpeciesCount

measureOf

Species

hasSpecies

RatioUnit

hasUnit

RatioValue

hasValue

DateTime SBLTERSite

hasTime hasLoc







• Species• …



A System for Semantic Integration of Geologic Maps via Ontologies

Kai Lin Bertram Ludäscher


Geologic Map Integration

• Given: – Geologic maps from different state geological

surveys (shapefiles w/ different data schemas)– Different ontologies:

• Geologic age ontology• Rock classification ontologies:

– Multiple hierarchies (chemical, fabric, texture, genesis) from Geological Survey of Canada (GSC)

– Single hierarchy from British Geological Survey (BGS)

• Problem– Support uniform queries using different

ontologies– Support registration w/ ontology A, querying w/

ontology B



domainknowledge

domainknowledge

Knowledge r

epresentatio

n

Ontologies!?

NevadaNevada

Geoscientists + Computer Scientists Igneous Geoinformaticists+/- Energy

GEON Metamorphism Equation:

+/- a few hundred million years


A Multi-Hierarchical Rock Classification Ontology (GSC)

Composition

Genesis

Fabric

Texture



Implementation in OWL: Not only “for the machine” …



System Overview

Data

Data

Data

Data

ontology A

ontology C

ontology B

Ontology enabled Map Integrator {A,B}

Application (B)

Application (C)

“semantic registration”

Data sets Ontologies Applications



Ontology Repository

• Accept user-defined ontologies in OWL

• Any ontology saved in the system can be imported into a user-defined ontology ( inter-ontology references)

• Provide tool to browse the ontologies in the repository

……………..<owl:Ontology> <owl:imports rdf:resource= "http://compute5.sdsc.geongrid.org:8080/workbench/jsp/ontologies/genesis.owl" /></owl:Ontology>…………….<owl:Class rdf:ID="Ultramafite"> <rdfs:subClassOf rdf:resource="#Ultramafic"/> <rdfs:subClassOf rdf:resource= "http://compute5.sdsc.geongrid.org:8080/workbench/jsp/ontologies/genesis.owl#Igneous"></owl:Class>……………..

composition.owl



Ontology Mapping: Motivation

• Align ontologies• Integrate data sets which are registered to different

ontologies• Query data sets through different ontologies • Ontology parameterization

Data set 1

Data set 2

Ontology 1

Ontology 2

register

register

Ontology mappings queries



Ontology Mapping: Definition

An ontology mapping consists of :

• a class mapping f:

• a property mapping g:

a partial mapping from the property set of Oa to theproperty set of Ob such that if p is a property betweenA1 and A2 in Oa, then g(p) is a property between f (A1) and f(A2) in Ob

a partial mapping from the class set of Oa to theclass set of Ob preserving the subclass relation

A1

A2

f(A1)

f(A2)

p g(p)

Oa Ob



Ontology Mapping: Combining Ontologies

The result O of combining ontologies Oa and Ob is a pushout of the following ontology mappings f and g :

Oc Oa

Ob O Example:

A

B1

A1

A2

B2A

A2

B2q

p

p

q

Oa

Ob

Oc

O



Ontology Switching

Given an ontology mapping f from Oa to Ob, Oa can be used to query any data sets which are registered to Ob.

Data set 1

Data set 2

Ontology Ob

Ontology Oa

register

register

Ontology mapping queries



Geology Workbench : Initial State

click on Ontologies click on Datasets click on Applications

An Ontology-based Mediator



Geology Workbench: Uploading Ontologies

click on Ontology SubmissionChoose an OWL file to uploadClick to check its detail Name SpaceCan be used to import this

ontology into others



Geology Workbench: Data (to Ontology!) RegistrationStep 1: Choose Classes

Click on Submission Data set name

Select a shapefile

Choose an ontology class



Geology Workbench: Data RegistrationStep 2: Choose Columns for Selected Classes

AREA

PERIMETER

AZ_1000

AZ_1000_ID

GEO

PERIOD

ABBREV

DESCR

D_SYMBOL

P_SYMBOL

It contains information about geologic age



Geology Workbench: Data RegistrationStep 3: Resolve Mismatches

Two terms arenot matched anyontology terms

Manually mappingalgonkian intothe ontology



Geology Workbench: Ontology-enabled Map Integrator

Click on the nameChoose interesting

Classes

All areas with the age Paleozoic



Geology Workbench: Change Ontology

Submit a mapping

Ontology mappingbetween British Rock

Classification and CanadianRock Classification

Switch from Canadian Rock Classification to

British Rock Classification

Run it New query interface


Back to Scientific Workflows, Kepler (and yes, web services…)


Web Services & Scientific Workflows in Kepler

• Web services = individual components (“actors”)• “Minute-Made” Application Integration:

– Plugging-in and harvesting web service components is easy and fast

• Rich SWF modeling semantics (“directors” and more):– Different and precise dataflow models of computation– Clear and composable component interaction semantics Web service composition and application integration tool

• Coming soon:– Shrinked wrapped, pre-packaged “Kepler-to-Go” (v0.8)– SWFs with structural and semantic data types (better design

support)– Grid-enabled web services (for big data, big computations,…) – Different deployment models (SWF WS, web site, applet, …)



Genomics Example: Promoter Identification Workflow

Source: Matt Coleman (LLNL)Source: Matt Coleman (LLNL)



Ecology: GARP Analysis Pipeline for Invasive Species Prediction

Training sample

(d)

GARPrule set

(e)

Test sample (d)

Integrated layers

(native range) (c)

Speciespresence &

absence points(native range)

(a)EcoGridQuery

EcoGridQuery

LayerIntegration

LayerIntegration

SampleData

+A3+A2

+A1

DataCalculation

MapGeneration

Validation

User

Validation

MapGeneration

Integrated layers (invasion area) (c)

Species presence &absence points

(invasion area) (a)

Native range

predictionmap (f)

Model qualityparameter (g)

Environmental layers (native

range) (b)

GenerateMetadata

ArchiveTo Ecogrid

RegisteredEcogrid

Database

RegisteredEcogrid

Database

RegisteredEcogrid

Database

RegisteredEcogrid

Database

Environmental layers (invasion

area) (b)

Invasionarea prediction

map (f)

Model qualityparameter (g)

Selectedpredictionmaps (h)

Source: NSF SEEK (Deana Pennington et. al, UNM)Source: NSF SEEK (Deana Pennington et. al, UNM)



Source: NIH BIRN (Jeffrey Grethe, UCSD)Source: NIH BIRN (Jeffrey Grethe, UCSD)



KEPLER Core Capabilities (1/2)

• Capturing scientific workflows– Accessing available workflows through the Grid

• Designing scientific workflows– Composition of actors (tasks) to perform a scientific WF

• Actor prototyping• Accessing heterogeneous data

– Data access wizard to search and retrieve Grid-based resources– Relational DB access and query– Ability to link to EML data sources



KEPLER Core Capabilities (2/2)

• Data transformation actors to link heterogeneous data

• Executing scientific workflows– Distributed and/or local computation– Various models for computational semantics and

scheduling– SDFSDF and PNPN: Most common for scientific workflows

• External computing environments:– C++, Python, C (… Perl--planned ...)

• Deploying scientific tasks and workflows as web services (… planned …)



The KEPLER GUI (Vergil)

Drag and drop utilities, director and actor libraries.



Running the workflow



Distributed SWFs in KEPLER

• Web and Grid Service plug-ins– WSDL, GWSDL– ProxyInit, GlobusGridJob, GridFTP, DataAccessWizard

• WS Harvester– Imports all the operations of a specific WS (or of all the WSs in a UDDI repository) as Kepler actors

• WS-deployment interface (…ongoing work…)• XSLT and XQuery transformers to link non-fitting

services together



A Generic Web Service Actor

Given a WSDL and the name of an operation of a web service, dynamically customizes itself to implement and execute that method.

Configure - select service operation



Set Parameters and Commit

Set parameters and commit



WS Actor after Instantiation



Web Service Harvester

• Imports the web services in a repository into the actor library.• Has the capability to search for web services based on a keyword.



Composing 3rd-Party WSs

Output of previousweb service

User interaction &Transformations

Input of next web service



GEON Kepler Examples

• Geon Classifier (Efrat) A workflow for classifying igneous rocks.

• Geologic Map Information Integration A workflow for map rendering using web

services(created by Ilkay and Ashraf).

• Database Access (Efrat) Generic actors for connecting and querying

a database.



Problem Description

• Classification of Igneous rocks• Data sets

– Virginia rock database (provides mineral composition).

– Igneous rock diagrams and a transition table for traversing between diagrams.

• Method– Iterations of finer descriptive levels using a

point-in-polygon algorithm.



British Classification of Igneous Rocks



Mineral Classification of Igneous Rocks

• Inputs:– A row id from the Virginia rock database (contains

mineral composition).– A dataset of diagrams for classification.

• Outputs:– The rock name.– A browser display of each classification level. A

new feature added in Kepler.

• Execution:– Divided into levels. Each provides a finer level of

granularity. – At each level, a point is classified within a diagram

using a PointInPolygon algorithm.



Classifying with Kepler

Extract mineral composition for row Id.

Igneous Rock Diagrams information.

Rock Name.




Diagrams information and transitions between them.

Extracted from the mineral composition and this level’s diagram coordinates.

SVG to polygons.

Classifier: Locates the point’s region.

Finer granularity

Displays the point in the diagram for this level.




• Ontology-enabled Map Integration (OMI)– Integration of Heterogeneous Geological Datasets

• Data sets – State geology map datasets (rocky mountain area)– State boundaries and coast lines.

• Rock Type Ontologies



SWF Designed in Kepler



DataMapper Sub-Workflow



Result launched via the BrowserUI actor


Providing DB Access through Kepler

• Database connection actor: – Opening a database connection and passing it to all actors

accessing this database.

• Database query actor:– A generic actor that queries a database and provides its

result.

• DBConnection type and DBConnectionToken:– A new IOPort type and a token to distinguish a database

connection from any general type.

Database Connection Actor

OpenDBConnection actor:

• Input: database connection information.• Output: A DBConnectionToken, a reference to a

database connection instance, through a DBConnection output port.

Database Query Actor

Database Query actor:

Input: A query string (SQL) and a database connection reference.

Parameters: output type – XML, Record or String. output each row separately or all at once.

Process: Execute query. Produce results according to parameters.

Querying Example


KEPLER and YOU

• Kepler …– is a community-based, cross-

project, open source collaboration

– uses web services as basic building blocks

– has a joint CVS repository, mailing lists, web site, …

– is gaining momentum thanks to contributors and contributions

• BSD-style license allows commercial spin-offs

– a pre-packaged, shrink-wrapped version (“Kepler-to-GO”) coming soon to a place near you…


semantic mediation, ontologies and scientific workflows and all the rest (+/– web services)

Documents

data registration

brooks ptolemy

lee ptolemy

datamanagement viewgenerally

ecological metadatamore

ecological analysis

semantic typeslabel

measurement scale