Science Environment for Ecological Knowledge Bertram Ludäscher San Diego Supercomputer Center University of California, San Diego UC Santa Barbara UC San Diego U New Mexico U Kansas Vermont, Napier, ASU, UNC

SEEK Overview, 3/ Architecture Overview Analysis & Modeling System –Design and execution of ecological models and analysis –End user focus – application-/upperware Semantic Mediation System –Data Integration of hard- to-relate sources and processes –Semantic Types and Ontologies – upper middleware EcoGrid –Access to ecology data and tools – middle-/underware Plus Working Groups: – Knowledge Representation (SEEK-KR) – Classification and Nomenclature (TAXON) – Biodiversity and Ecological Analysis and Modeling (BEAM) (cf. GEON + Cyberinfrastructure)

SEEK Overview, 3/ SEEK EcoGrid Goal: standardize interfaces (using web and grid services) –We have standardized data via EML –Integrate diverse data networks from ecology, biodiversity, and environmental sciences Grid-standardized interfaces –Uniform interface to: Metacat, SRB, DiGIR, Xanthoria, etc. Anyone can implement these interfaces Hides complexity of underlying systems Metadata-mediated data access –Supports multiple metadata standards –EML, Darwin Core as foci Computational services –Pre-defined analytical services –On-the-fly analytical services

SEEK Overview, 3/ Grid versus Web Services Grid Services are Web Services –Add authentication, lifecycle management, notification, etc. –Globus Toolkit 3: Implements Open Grid Services Architecture (OGSA) Implications for use –Write a normal web service extending GridService base class –When deployed within GT3, you get these extra functions for ‘free’ –Supports distributed computation via proxy authentication Problems –Complex system to understand –GT3 can be difficult to deploy –Proposals to incorporate grid services within the Web services community (Web Services Resource Framework [WSRF])

SEEK Overview, 3/ EcoGrid client interactions Modes of interaction –Client-server –Fully distributed –Peer-to-peer EcoGrid Registry –Node discovery –Service discovery Aggregation services –Centralized access –Reliability –Data preservation

SEEK Overview, 3/ Building the EcoGrid ANDLUQHBRNTL 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

SEEK Overview, 3/ Kepler: Scientific Workflows EML provides semi-automated data binding Scientific workflows represent knowledge about the process; Kepler captures this knowledge Query EcoGrid to find data Archive output to EcoGrid

SEEK Overview, 3/ GARP Invasive Species Model Training sample (d) GARP rule set (e) Test sample (d) Integrated layers (native range) (c) DiGIR Species presence & absence points (native range) (a) EcoGrid Query EcoGrid Query Layer Integration Layer Integration Sample + A3 + A2 + A1 Data Calculation MapValidation User ValidationMap SRB Environmental layers (invasion area) (b) Integrated layers (invasion area) (c) Invasion area prediction map (f) DiGIR Species presence &absence points (invasion area) (a) Native range prediction map (f) Model quality parameter (g) SRB Environmental layers (native range) (b) Model quality parameter (g) Slide from D. Pennington Scientific workflows represent knowledge about the process; AMS captures this knowledge

SEEK Overview, 3/ 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 Bertram Ludäscher BIRN, GEON, SDM, SEEK Steve Mock NMI Steve Neuendorffer Ptolemy II Jing Tao SEEK Mladen Vouk SDM Yang Zhao Ptolemy II … Ptolemy II

SEEK Overview, 3/ Kepler Understands EML Data (Chad Berkley, SEEK)

SEEK Overview, 3/ Kepler: Ecological Modeling (Chad Berkley, SEEK)

SEEK Overview, 3/ Database Access (Efrat Jaeger, GEON) Note: EML descriptions of relational sources would allow automated data ingestion

SEEK Overview, 3/ Mineral Classification with Kepler … (Efrat Jaeger, GEON)

SEEK Overview, 3/ … inside the Classifier

SEEK Overview, 3/ Standard BrowserUI: Client-Side SVG

SEEK Overview, 3/ SWF Reengineering (Ilkay, SDM; Ashraf, Efrat, Kai, GEON)

SEEK Overview, 3/ DataMapper Sub-Workflow

SEEK Overview, 3/ Result launched via BrowserUI actor (coupling with ESRI’s ArcIMS)

SEEK Overview, 3/ Distributed Workflows in KEPLER Web and Grid Service plug-ins –WSDL (now) and Grid services (stay tuned …) –ProxyInit, GlobusGridJob, GridFTP, DataAccessWizard –SSH, SCP, SDSC SRB, OGS?-???… coming WS Harvester –Import query-defined WS operations as Kepler actors XSLT and XQuery Data Transformers –to link not “designed-to-fit” web services WS-deployment interface (planned)

SEEK Overview, 3/ Web Service Actor (Ilkay Altintas, SDM) 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

SEEK Overview, 3/ Set Parameters and Commit Set parameters and commit

SEEK Overview, 3/ Specialized WS Actor (after instantiation)

SEEK Overview, 3/ Web Service Harvester (Ilkay Altintas, SDM) Imports the web services in a repository into the actor library. Has the capability to search for web services based on a keyword.

SEEK Overview, 3/ Kepler: Grid Services Access (Steve Mock, NMI)

SEEK Overview, 3/ An (oversimplified) Model of the Grid Hosts : {h1, h2, h3, …} Hosts : i }, j }, … Hosts : i }, j }, … Given : data/workflow: … as a functional plan: […; Y := f(X); Z := g(Y); …] … as a logic plan: […; f(X,Y)  g(Y,Z); …] Find Host Assignment : d i  h i, f j  h j for all d i, f j … s.t. […; := …] is a valid plan f g X Y Z

SEEK Overview, 3/ Shipping & Handling Algebra (SHA) plan = of = 1.[ to A, := to C ] 2.[ => B, := to C ] 3.[ to C, => C, := ] Logical view Physical view: SHA Plans (1) (3) (2)

SEEK Overview, 3/ Grid-Enabling PTII: Handles AB GAGB 1.A  GA: get_handle 2.GA  A: return &X 3.A  B: send &X 4.B  GB: request &X 5.GB  GA: request &X 6.GA  GB: send *X 7.GB  B: send done(&X) Example : &X = “GA.17” *X = Candidate Formalisms : GridFTP SSH, SCP SDSC SRB OGS?-??? … WSRF? Kepler space Grid space Logical token transfer (3) requires get_handle(1,2); then exec_handle(4,5,6,7) for completion.

SEEK Overview, 3/ Homogeneous Data Integration Integration of homogeneous or mostly homogeneous data via EML metadata is relatively straightforward

SEEK Overview, 3/ 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

SEEK Overview, 3/ 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 DataOntologyWorkflow Components

SEEK Overview, 3/ 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

SEEK Overview, 3/ Extensions : Semantic Types Take concepts and relationships from an ontology to “semantically type” the data-in/out ports Application: e.g., design support: –smart/semi-automatic wiring, generation of “massaging actors” m 1 (normalize) p3p3 p4p4 Takes Abundance Count Measurements for Life Stages Returns Mortality Rate Derived Measurements for Life Stages

SEEK Overview, 3/

SEEK Overview, 3/

SEEK Overview, 3/ Semantic Types The semantic type signature –Type expressions over the (OWL) ontology m 1 (normalize) p3p3 p4p4 SemType m1 :: Observation & itemMeasured.AbundanceCount & hasContext.appliesTo.LifeStageProperty -> DerivedObservation & itemMeasured.MortalityRate & hasContext.appliesTo.LifeStageProperty

SEEK Overview, 3/ Extended Type System (here: OWL Semantic Types) SemType m1 :: Observation & itemMeasured.AbundanceCount & hasContext.appliesTo.LifeStageProperty  DerivedObservation & itemMeasured.MortalityRate & hasContext.appliesTo.LifeStageProperty Substructure association: XML raw-data =(X)Query=> object model =link => OWL ontology

SEEK Overview, 3/ Semantic Types for Scientific Workflows

SEEK Overview, 3/ Deriving Data Transformations from Semantic Service Registration [Bowers-Ludaescher, DILS’04]

SEEK Overview, 3/ Structural and Semantic Mappings [Bowers-Ludaescher, DILS’04]

SEEK Overview, 3/ Fundamental improvements for researchers –Global access to ecologically relevant data –Rapidly locate and utilize distributed computation –Capture, reproduce, extend analysis process SEEK Impact

SEEK Overview, 3/ Acknowledgements This material is based upon work supported by: The National Science Foundation under Grant Numbers , , , , , and PBI Collaborators: NCEAS, University of New Mexico (Long Term Ecological Research Network Office), San Diego Supercomputer Center, University of Kansas (Center for Biodiversity Research) Kepler contributors: SEEK, Ptolemy II, SDM/SciDAC, GEON