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Overview of the Science Environment for Ecological Knowledge (SEEK) Ricardo Scachetti Pereira.

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Presentation on theme: "Overview of the Science Environment for Ecological Knowledge (SEEK) Ricardo Scachetti Pereira."— Presentation transcript:

1 Overview of the Science Environment for Ecological Knowledge (SEEK) Ricardo Scachetti Pereira (with many, many slides from Matt Jones, Bertram Ludäscher, Ilkay Altintas, Chad Berkeley and others) University of Kansas, USA June 30, 2005

2 SWDBAug 29, 2004 June, 2005 Outline Introduction to SEEK Introduction to Kepler Kepler capabilities and sample workflows Current and future developments

3 SWDBAug 29, 2004 June, 2005 What is SEEK? Science Environment for Ecological Knowledge Multidisciplinary project to create: Scientific-workflow system (Kepler) –Design, document, reuse, and execute scientific analyses Distributed data network (EcoGrid) –Environmental, ecological, and systematics data Knowledge Representation & Semantic Mediation –Discover, integrate, and compose hard-to-relate data and services via ontologies Taxonomic, Biology, and Education subcomponents Collaborators (the SEEK team) NCEAS, UNM, SDSC/UCSD, U Kansas, UC Davis Vermont, Napier, ASU, UNC

4 SWDBAug 29, 2004 June, 2005 Scientific Workflows Model the way scientists work with their data now –Mentally coordinate export and import of data among software systems 1)Capture data in the field 2)Digitize it into Excel spreadsheets 3)Export as CSV files 4)Import into statistical package 5)Perform analysis 6)Export results, tables and graphics 7)Write and publish article Query EcoGrid to find data Archive output to EcoGrid with workflow metadata

5 SWDBAug 29, 2004 June, 2005 Scientific Workflows Scientific workflows are: –Not linear –Involve multiple data sets –Involve multiple analytical steps

6 SWDBAug 29, 2004 June, 2005 Metadata driven data ingestion Key information needed to read and machine process a data file is in the metadata –File descriptors (CSV, Excel, RDBMS, etc.) –Entity (table) and Attribute (column) descriptions Name Type (integer, float, string, etc.) Codes (missing values, nulls, etc.) In the future, this will include semantic typing

7 SWDBAug 29, 2004 June, 2005 Metadata driven data ingestion Metadata is revised following any transformation Versioning of metadata and data is very important This process results in a lineage of the data file as it has been transformed

8 SWDBAug 29, 2004 June, 2005 Data integration Integration of heterogeneous data requires much more advanced metadata and processing –Attributes must be semantically typed –Collection protocols must be known –Units and measurement scale must be known –Measurement mechanics must be known (i.e. that Density=Count/Area) –This is an advanced research topic within the SEEK project

9 SWDBAug 29, 2004 June, 2005 Label data with semantic types Label inputs and outputs of analytical components with semantic types Use SMS to generate transformation steps –Beware analytical constraints Use SMS to discover relevant components Ontology = specification of a conceptualization (a knowledge map) Semantic typing DataOntologyWorkflow Components

10 SWDBAug 29, 2004 June, 2005 SEEK Components Revisited

11 SWDBAug 29, 2004 June, 2005 SEEK EcoGrid Goal: allow diverse environmental data systems to interoperate –Hides complexity of underlying systems using lightweight interfaces –Integrate diverse data networks from ecology, biodiversity, and environmental sciences Data systems –Any system can implement these interfaces –Prototyping using: Metacat, SRB, DiGIR, Xanthoria, etc. Supports multiple metadata standards –EML, Darwin Core as foci Implemented as OGSA Grid Services –Query() –Get() –Put() –Login() –… Tiered-implementation critical to adoption

12 SWDBAug 29, 2004 June, 2005 Kepler: Scientific Workflows Implements the workflow system in SEEK Open, collaborative effort of: –SEEK, SciDAC/SDM, GEON, Ptolemy Project –Ecology, biodiversity, molecular bio, geology, engineering Based on Ptolemy II system Kepler aims to extend the Ptolemy system with: –Web and grid service access –Data integration support –Semantic reasoning Kepler actors are written in Java but can wrap other applications (such as MATLAB, GRASS) Actors can call arbitrary Web (or Grid) Services Ptolemy already has a very large inventory of actors

13 SWDBAug 29, 2004 June, 2005 Actor Search and Browse Actors Panel –Large number of actors –Organized hirarchically –Search makes it easy to find right actor –Ontology-based Plan to support multiple views

14 SWDBAug 29, 2004 June, 2005 EcoGrid: EML Data Access

15 SWDBAug 29, 2004 June, 2005 EcoGrid: Queries

16 SWDBAug 29, 2004 June, 2005 EcoGrid: Queries

17 SWDBAug 29, 2004 June, 2005 EML Metadata Display

18 SWDBAug 29, 2004 June, 2005 EcoGrid: DarwinCore Access

19 SWDBAug 29, 2004 June, 2005 Kepler: database access

20 SWDBAug 29, 2004 June, 2005 Kepler: web service example

21 SWDBAug 29, 2004 June, 2005 Kepler: grid services access

22 SWDBAug 29, 2004 June, 2005 Kepler: ecological modeling

23 SWDBAug 29, 2004 June, 2005 New ENM Workflow

24 SWDBAug 29, 2004 June, 2005 Data Analysis: Biodiversity Indices

25 SWDBAug 29, 2004 June, 2005 R in Kepler Source: Dan Higgins, Kepler/SEEK

26 SWDBAug 29, 2004 June, 2005 ORB

27 SWDBAug 29, 2004 June, 2005 Kepler today Supports scientific workflows –Ecology, molecular bio, geology, … –Variety of analytical components (including spatial data transformations) –Support for R scripts and Matlab scripts EcoGrid access to heterogeneous data –EML Data support Experimental data, survey data, spatial raster and vector data, etc. –DarwinCore Data support Museum collections –EcoGrid registry to discover data sources Ontology-based browsing for analytical components –Exploit semantics to improve the user experience Demonstration workflows –Ecology: Ecological Niche Modeling –Genomics: Promoter Identification Workflow –Geology: Geologic Map Information Integration –Oceanography: Real-time Revelle example of data access

28 SWDBAug 29, 2004 June, 2005 Kepler this year Usability engineering –Full evaluation and user-oriented customization of all UI components Distributed computing/grid computing –Large jobs, lots of machines –Detached execution Component repository / downloadable components Smart data and component discovery –Support annotating data sources Automated data and service integration and transformation using ontologies Complete EcoGrid access –Full EML support –Support for large data and 3 rd -party transfer –More data sources and types of data sources (e.g., JDBC, GEON data) Provenance and metadata propagation

29 SWDBAug 29, 2004 June, 2005 Acknowledgements This material is based upon work supported by: The National Science Foundation under Grant Numbers , , , , , and Collaborators: NCEAS (UC Santa Barbara), University of New Mexico (Long Term Ecological Research Network Office), San Diego Supercomputer Center, University of Kansas (Center for Biodiversity Research), University of Vermont, University of North Carolina, Napier University, Arizona State University, UC Davis The National Center for Ecological Analysis and Synthesis, a Center funded by NSF (Grant Number ), the University of California, and the UC Santa Barbara campus. The Andrew W. Mellon Foundation. Kepler contributors: SEEK, Ptolemy II, SDM/SciDAC, GEON


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