1. The Rise of Informatics as-a Research Domain WIRADA Science Symposium August 2, 2011, Melbourne Peter Fox (RPI and WHOI) pfox@cs.rpi.edupfox@cs.rpi.edu Tetherless World Constellation

2. 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

3. 3 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, …

4. 4 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.

5. Data integration and assimilation South Esk Flow Forecast (see talks by: )

6. Application integration! Smart faceted search Biological and chemical oceanography

7. Modern informatics enables a new scale-free** framework approach Use cases Stakeholders Distributed authority Access control Ontologies Maintaining Identity

8. Huh? Scale free? Citation networks, the Web, semantic networks

9. Use Case … is a collection of possible sequences of interactions between the system under discussion and its actors, relating to a particular goal.

10. Real use cases: Marine habitat - change Scallop, number, density Scallop, size, shape, color, place Scallop, shell fragment 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

11. Information Modeling Conceptual Logical Physical 11

12. 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

13. 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 Organizational Structure

14. 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

15. Data-Information- Knowledge Ecosystem 15 DataInformationKnowledge ProducersConsumers Context Presentation Organization Integration Conversation Creation Gathering Experience

16. 16 ProducersConsumers Quality Control Fitness for Purpose Fitness for Use Quality Assessment Trustee Trustor Others…

17. Working with knowledge Expressivity Maintainability/ Extensibility Implementability

18. Unit of exchange – the triple - example (linked data) Heath (2009) Closed World Open World

19. Working with knowledge Query Rule execution Inference

20. Expressivity/ Implementation Declarative Procedural Linked open data URI/http/RDF * Ontology encoded

21. Ontology Spectrum An ontology specifies a rich description of the Terminology, concepts, nomenclature Properties explicitly defining concepts Relations among concepts (hierarchical and lattice) Rules distinguishing concepts, refining definitions and relations (constraints, restrictions, regular expressions) relevant to a particular domain or area of interest. www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html slide from Kendall/McGuinness SemTech Tutorial

22. Standards - technical Credit: B. Rouse (BEVO) 2008 Data Systems

23. The social side Credit: B. Rouse (BEVO) 2008 User Group

24. What is the ecosystem? Many elements, and they are scattered But these are what enable scientists to explore/ confirm/ deny their research Accountability ProofExplanationJustificationVerifiability ‘Transparency’ -> Translucency Trust ‘Provenance’ Identity

25. 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

26. 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

27. 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

28. Quality Control vs. Quality Assessment Quality Control (QC) flags in the data (assigned by the algorithm) reflect “happiness” of the retrieval algorithm, e.g., all the necessary channels indeed had data, not too many clouds, the algorithm has converged to a solution, etc. (producer) Quality assessment is done by analyzing the data “after the fact” through validation, intercomparison with other measurements, self-consistency, etc. It is presented as bias and uncertainty. It is rather inconsistent and can be found in papers, validation reports all over the place. (consumer)

29. Information models

30. Integrating, mediating… At the conceptual level and under an open world assumption Conceptual modeling ontology (McCusker et al. 2011) -> bridging properties to SKOS, IAO,..

31. 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

32. Thanks… Questions? @taswegian pfox@cs.rpi.edu http://tw.rpi.edu