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Discover and access complex ecological data with ÆKOS
- 1. ÆKOS: A new paradigm for discovery and access to complex ecological data David Turner, Paul Chinnick, Andrew Graham, Matt Schneider, Craig Walker Logos used with consent. Content of this presentation except logos is released under TERN Attribution Licence Data Licence v1.0
- 2. Goal: To enable the preservation and appropriate re-use of rich ecological information Complexity: www.derm.qld.gov.au Data usually needs explanation and context before it can be accurately used Diversity: Source:Forestcheck: www.dec.wa.gov.au Ecological Data covers a wide range of topics © Alamay Fragmentation: Many different ways of measuring/observing/ expressing similar ecological concepts www.nswrail.net * Rapidly evolving Dispersal: Data is stored in many storage locations and formats
- 3. Needs and expectations of users An interface to allow for: • Data discovery • Immediate or facilitated data access • Comprehension A common information platform • Easy manipulation Familiar tools and or intuitive interface Cater for individuality • Each user will have different data requirements
- 4. The Information Landscape © eResearchSA © eResearchSA Plants Birds Bats
- 6. The ecological data delivery paradox IT solutions require rigid models to process data hence tend to force things into narrow structure – BUT flexible models are required to preserve the full richness of data and context • Software is not intelligent and requires fixed structures and fixed rules (logic) • Understanding data involves many subtleties which are complex to model • Models and processing dependent on the question
- 7. Towards the new paradigm Accept that flexible data needs flexible information models • A sparse semi-structured graph oriented knowledge model (not a data model) • Model must be extensible (ontology based) Create descriptive artifacts for consumers • Provide the understanding of context (express subtleties) and avoid the significant complexity of modelling By capturing the full richness, we enable informed re-use for the broadest range of questions • The user can assess suitability for their purpose and can obtain complete information
- 8. ÆKOS Operational Model Common Information Model (Ontology) Data Custodian Subject Expertise ETL Script Engine Data Data Ingestion ÆKOS Source Portal Data Portal Contextual ÆKOS DSL Description ÆKOS Repository
- 9. The user experience Build complex queries From simple blocks Site context Key features Access and licensing
- 10. The information model Select my site Visit 1 Establish it, and make some observations Make more Visit 2 observations Make more Visit 3 observations Etc...
- 11. The user experience Search level controls granularity Record instances e.g. visits Observation instances Observation instance details
- 12. Concept alignment •We integrate data from many different sources, using different classification systems •All datasets indexed with common set of search terms (vocabularies) •Taxonomy is a good example •Many other themes for search from project metadata to observation level data
- 13. Descriptive artefacts Context represents a communication challenge not a modelling problem • Standard structure • Bite sized chunks Image of descriptive doc Once available • Direct linkage between data and description • Authored synthesis based on information derived from multiple sources (published and unpublished)
- 14. Current status
- 15. Live system demo... Members Dining Room 3 15:30 – 17:10
Notas do Editor
- Data Enrichment - Transfer source database, enrich the context by utilising the strong relationship between Agency and Eco-informatics, to add knowledge from the custodial expert to the dataData Integration – map data into a common structure that reflects to reality of data using an ontological model rather than a system-specific approachSemantics – all fields and all vocabulary concepts have definitions for clarity. Can search across data which is different in the source but similar in ÆKOSThe ÆKOS DSL is the centrepiece of the system. This defines the NoSQL model, identifies inconsistences resulting from information model changes and re-aligns data to extended or revised model. This approach supports a constantly changing or evolving model – the domain is not static so no schema – there is continuous change. DSL also provides the framework for the portal products as it pre-populates the search index, graph visualisation and description. This means that the portal products are defined on the fly rather than be hard coded.