Applying Computer Science Research in Biodiversity Informatics

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Applying Computer Science Research in
Biodiversity Informatics

• Some Experiences and Lessons
• Andrew Jones
• Cardiff University
• Andrew.C.Jones_at_cs.cardiff.ac.uk

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Example predicting effect of climate change

• Typical question Where might a species be
  expected to occur, under present or predicted
  climatic conditions?
• Some relevant resource types
• Data sources
• Catalogue of life
• Species Information Sources (SISs)
• Species geography
• Descriptive data
• Specimen distribution
• Geographical
• Boundaries of geographical political units
• Climate surfaces
• Genetic sequences
• Analytic tools
• Biodiversity richness assessment various
  metrics
• Bioclimatic modelling bioclimatic envelope
  generation
• Phylogenetic analysis (generation of phylogenetic
  trees)

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Some potential problems

• Availability of data
• Originally constructed for various reasons
  proprietary standards no interoperability
• Even if on Internet, may be difficult to find or
  use
• Heterogeneity
• Representation
• Granularity
• Scientific naming issues
• Flexibility scalability

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LITCHI background

• BBSRC/EPSRC- and EU- funded
• Aim to detect conflicts between species
  checklists and either
• Assist in producing a consistent checklist, or
• Generate correspondences between checklists
  (cross-map)
• Addressed problems presented by species
  classification and naming variations when
  accessing data relating to species

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LITCHI example

• Checklist 1
• Caragana arborescens Lam. (accepted name)
• Caragana sibirica Medikus (synonym)

• Checklist 2
• Caragana sibirica Medikus (accepted name)
• Caragana arborescens Lam. (synonym)
• (Lam. Lamark)

A full name which is not a pro-parte name may
not appear as both an accepted name and a synonym
in the same checklist
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Issues in LITCHI

• Used constraints (implemented in Prolog) on what
  constituted a consistent checklist
• Repair standard techniques for integrity repair
  fail to generate some important possible ways of
  repairing violations
• Our approach to add information that allows us
  to distinguish between categories of constraint
  violation
• Sicstus Prolog/Visual Basic/Microsoft Access
  solution only worked on some PCs(!)
• So we reimplemented in Java

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SPICE for Species 2000

• Initial project funded by BBSRC/EPSRC follow-on
  by EU
• The SPICE for Species 2000 project aimed to
• build a federated catalogue of scientific names
  organised by taxon (species, etc.)
• accommodate GSD (Global Species Database)
  heterogeneity, autonomy instability
• ensure scalability
• Addressed availability of dataheterogeneity of
  representation

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SPICE internal architecture
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Implementing SPICE

• Techniques have included
• Common Data Model
• Tightly coupled federation
• Small set of supported request types (canned
  queries)
• CORBA and HTTP (CGI)/XML implementations
• Issues
• Trade-off between scalability and ease of
  deployment
• CORBA
• Scaled up well
• Useful platform for CAS load balancing
• Firewall problems heavyweight, unfamiliar
  technique for GSD providers
• HTTP/XML
• Less scalable
• Easily deployed(Some providers simply had to
  modify their existing Webfront end code)

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GRAB (GRid And Biodiversity)

• 6 month DTI-funded demonstrator project
• Project aim
• Assess Grids potential for collaborative
  research in biodiversity informatics
• Supporting discovery use of diverse
  biodiversity-related databases
• Exploring use of Globus SRB middleware

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GRAB resource types
Catalogueof life
SIS
Climate
SIS
...
GRAB resource clients
GRAB interface

• Catalogue of life
• Scientific common names
• Species Information System (SIS)
• Images geography
• Climate
• Max/min temperature annual precipitation

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Issues in GRAB

• Problems installing Globus research software
• Essentially wanted to send distributed requests
  receive responses
• Initial HTTP-based prototype worked well
• Versions of SRB then available had little to
  offer
• Globus 2 approach needed canned queries,
  temporary files, etc much more difficult than
  the HTTP prototype

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The BiodiversityWorld project

• 3 year e-Science project funded by BBSRC
• Aim
• Build a Biodiversity Grid(Problem Solving
  Environment to support Biodiversity research)

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BiodiversityWorld architecture


User interface


Presentation

Workflow
enactment
Wrapped
Native

engine

resources

Biodiversity
-
Metadata
World
repositor
y

Resources

BGI API


BiodiversityWorld
-
GRID
Interface
(BGI)


The GRID

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Issues in BDW

• Globus 3 provides Grid Services, but still
  evolving (WSRF in Globus 4)
• Trade-off abstraction layer (BGI) including
  invocation mechanism
• Insulates from change
• Wraps resources to remove needless heterogeneity
• Wraps the wrapped resources (!) to insulate from
  infrastructure change
• Performance penalty
• Assume computationally intensive applications lie
  in a single BDW resource
• Hinders interoperation with other
  Grid/Webservices

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Summary

• We have applied
• modern, complex commercial software
• specialised research software, and
• Computer Science theory
• to address biodiversity informatics problems
• In practice, real-world applications often
  bring limitations in such software techniques
  to light, necessitating (e.g.) compromises,
  trade-offs, work-arounds, extensions to theory,

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Acknowledgements

• UK DTI, EPSRC BBSRC EU
• Collaborators on grants mentioned Universities
  of Southampton and Reading Natural History
  Museum (London)
• Organisations that have co-operated with these
  research projects, especially
• Species 2000
• ILDIS
• FishBase
• Hadley Centre for Climate Prediction and Research