The Rise of Informatics as-a Research Domain

WIRADA Science Symposium

August 2, 2011, Melbourne

Peter Fox (RPI and WHOI) pfox@cs.rpi.eduTetherless World Constellation

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.

Application integration!

• Smart faceted search

Biological and chemical oceanography

Modern informatics enables a new scale-free** framework approach

• Use cases• Stakeholders• Distributed

authority• Access control• Ontologies• Maintaining

Identity

Huh? Scale free?

Citation networks, the Web, semantic networks

Use Case• … is a collection of possible sequences of

interactions between the system under discussion and its actors, relating to a particular goal.

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

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

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

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

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…

Working with knowledge

Expressivity

Maintainability/ Extensibility

Implementability

Unit of exchange – the triple - example (linked data)

Heath (2009)

Closed WorldOpen World

Working with knowledge

Query

Rule execution

Inference

Expressivity/ Implementation

Declarative Procedural

Linked open dataURI/http/RDF *

Ontology encoded

Standards - technical

Credit: B. Rouse (BEVO) 2008

Data Systems

The social side

Credit: B. Rouse (BEVO) 2008

User Group

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

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

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

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

Information models

Integrating, mediating…

• At the conceptual level and under an open world assumption

Conceptual modeling ontology (McCusker et al. 2011) -> bridging properties to SKOS, IAO, ..

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

Thanks… Questions?

• @taswegian

• pfox@cs.rpi.edu

• http://tw.rpi.edu