The Rise of Informatics as-a Research Domain
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The Rise of Informatics as-a Research Domain
WIRADA Science Symposium
August 2, 2011, Melbourne
Peter Fox (RPI and WHOI) [email protected] World Constellation
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What’s ahead (today)
• Do you need motivation? – If so - Data Science and Informatics
• An example
• Rising = maturity = repeating it – from technology to methodology– Use cases, information models and more …
• Research topics
• Where is informatics rising to?
2Tetherless World Constellation
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Working premise
Scientists – actually ANYONE - should be able to access a global, distributed knowledge base of scientific data that:• appears to be integrated• appears to be locally available
Data – volume, complexity, mode, scale, heterogeneity, …
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Mind the Gap!
• There is/ was still a gap between science
and the underlying infrastructure and
technology that is available
• Cyberinfrastructure is the new research environment(s) that support advanced data acquisition, data storage, data management, data integration, data mining, data visualization and other computing and information processing services over the Internet.
Informatics - information science includes the
science of (data and) information, the practice
of information processing, and the engineering
of information systems. Informatics studies the
structure, behavior, and interactions of natural
and artificial systems that store, process and
communicate (data and) information. It also
develops its own conceptual and theoretical
foundations. Since computers, individuals and
organizations all process information,
informatics has computational, cognitive and
social aspects, including study of the social
impact of information technologies. Wikipedia.
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Application integration!
• Smart faceted search
Biological and chemical oceanography
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Modern informatics enables a new scale-free** framework approach
• Use cases• Stakeholders• Distributed
authority• Access control• Ontologies• Maintaining
Identity
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Huh? Scale free?
Citation networks, the Web, semantic networks
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Use Case• … is a collection of possible sequences of
interactions between the system under discussion and its actors, relating to a particular goal.
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Real use cases:Marine habitat - change
Scallop,number,density
Scallop, size,shape, color,place
Scallop,shellfragment
Rock
What is this?
Flora or fauna?
Dirt/ mud; one person’s noise is another person’s signal
Several disciplines; biology, geology, chemistry, oceanography
Several applications; science, fishing, habitat change, climate and environmental change, data integration
Complex inter-relations, questions
Use case: What is the temperature and salinity of the water and are these marine specimens usual or part of an ecosystem change?
Src: WHOI and the HabCam group
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Information Modeling
• Conceptual
• Logical
• Physical
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Socio-technical system(s)
• Refers to the joint social and technical aspects of ‘systems’
• Sociological – people and groups of people
• Technical – more than technology but the two are often conflated – of organization and process
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Informatics efforts:
‘These members assume well defined roles and status relationships within the context of the virtual group that may be independent of their role and status in the organization employing them’ (Ahuja et al., 1998).
Technology
Communication Patterns
OrganizationalStructure
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Research domain
• Pulling apart the data/information/ knowledge ecosystem
• Capturing and representing knowledge– Closed world/ open world
• Standards – a socio-technical system• What, why, how – knowledge provenance
ecosystem (yes, another one)• Working with multiple information models
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Data-Information-Knowledge Ecosystem
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Data Information Knowledge
Producers Consumers
Context
PresentationOrganization
IntegrationConversation
CreationGathering
Experience
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Producers Consumers
Quality Control
Fitness for Purpose Fitness for Use
Quality Assessment
Trustee Trustor
Others… Others…
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Working with knowledge
Expressivity
Maintainability/ Extensibility
Implementability
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Unit of exchange – the triple - example (linked data)
Heath (2009)
Closed WorldOpen World
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Working with knowledge
Query
Rule execution
Inference
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Expressivity/ Implementation
Declarative Procedural
Linked open dataURI/http/RDF *
Ontology encoded
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Standards - technical
Credit: B. Rouse (BEVO) 2008
Data Systems
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The social side
Credit: B. Rouse (BEVO) 2008
User Group
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What is the ecosystem?
• Many elements, and they are scattered• But these are what enable scientists to
explore/ confirm/ deny their research
Accountability
ProofExplanation Justification Verifiability
‘Transparency’ -> Translucency
Trust
‘Provenance’
Identity
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Provenance
• Origin or source from which something comes, intention for use, who/what generated for, manner of manufacture, history of subsequent owners, sense of place and time of manufacture, production or discovery, documented in detail sufficient to allow reproducibility or who, what, where, why, when…
• Knowledge provenance; enrich with ontologies and ontology-aware tools
• Provenance presentation is a challenge
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Provenance Distance Computation
Based on provenance “distance”, we tell users how different data products are.
Issues:•Computing the similarity of two provenance traces is non-trivial
• Factors in provenance have varied weight on how comparable results of processing are
• Factors in provenance are interdependent in how they affect final results of processing
•Need to characterize similarity of external (vs. internal) provenance
•Dimensions/factors that affect comparability is quickly overwhelming
•Not all of these dimensions are independent - most of them are correlated with each other.
•Numerical studies comparing datasets can be used, when available, and where applicable to the analysis
Based on provenance “distance”, we tell users how different data products are.
Issues:•Computing the similarity of two provenance traces is non-trivial
• Factors in provenance have varied weight on how comparable results of processing are
• Factors in provenance are interdependent in how they affect final results of processing
•Need to characterize similarity of external (vs. internal) provenance
•Dimensions/factors that affect comparability is quickly overwhelming
•Not all of these dimensions are independent - most of them are correlated with each other.
•Numerical studies comparing datasets can be used, when available, and where applicable to the analysis
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Quality, Uncertainty, Bias
• Quality– Is in the eyes of the beholder – worst case scenario… or a good challenge
• Uncertainty– has aspects of accuracy (how accurately the real world situation is assessed, it
also includes bias) and precision (down to how many digits)
• Bias has at least two aspects:– Systematic error resulting in the distortion of measurement data caused by
prejudice or faulty measurement technique – A vested interest, or strongly held paradigm or condition that may skew the
results of sampling, measuring, or reporting the findings of a quality assessment:
• Psychological: for example, when data providers audit their own data, they usually have a bias to overstate its quality.
• Sampling: Sampling procedures that result in a sample that is not truly representative of the population sampled. (Larry English)
• Semantics – all about meaning in context (see diagram!)• Provenance = enabler but knowledge provenance = transformative
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Information models
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Integrating, mediating…
• At the conceptual level and under an open world assumption
Conceptual modeling ontology (McCusker et al. 2011) -> bridging properties to SKOS, IAO, ..
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Where to?
• Balancing research and application– Increase emphasis and presence in educational
organizations
• Confront the differences in incentives and inhibitions in different disciplines
• Further develop peer communities and organizations– Journal impact factors have to go up
• Explore the shift into open-world semantics and data frameworks
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Thanks… Questions?
• @taswegian
• http://tw.rpi.edu