Complexity must becomeLinear or Decrease
Smart data infrastructure: The sixth generation of mediation for data science
Peter Fox1 ([email protected])
(1Rensselaer Polytechnic Institute 110 8th St., Troy, NY, 12180 United States – see Acknowledgements)
Glossary:RPI – Rensselaer Polytechnic InstituteTWC – Tetherless World Constellation at Rensselaer Polytechnic InstituteS2S – S2S (!)SESF – Semantic eScience FrameworkBCO-DMO – Biological and Chemical Oceanography Data Management Office
Acknowledgments:SeSF Project Team: Eric Rozell, Han Wang, Jin Zheng, Patrick West, Stephan Zednik, Jim Hendler, Deborah McGuinnessBCO-DMO Staff: Cyndy Chandler, Adam Shephard, Bob Groman
Sponsors:National Science FoundationTetherless World Constellation
MOTIVATION
In the emergent “fourth paradigm” (data-driven) science, the scientific method is enhanced by the integration of significant data sources into the practice of scientific research.
To address Big Science, there are challenges in understanding the role of data in enabling researchers to attack not just disciplinary issues, but also the system-level, large-scale, and transdisciplinary global scientific challenges facing society.
Recognizing that the volume of data is only one of many dimensions to be considered, there is a clear need for improved data infrastructures to mediate data and information exchange, which we contend will need to be powered by semantic technologies.
One clear need is to provide computational approaches for researchers to discover appropriate data resources, rapidly integrate data collections from heterogeneously resources or multiple data sets, and inter-compare results to allow generation and validation of hypotheses.
Another trend is toward automated tools that allow researchers to better find and reuse data that they currently don’t know they need, let alone know how to find. Again semantic technologies will be required.
Finally, to turn data analytics from "art to science", technical solutions are needed for cross-dataset validation, reproducibility studies on data-driven results, and the concomitant citation of data products allowing recognition for those who curate and share important data resources.
Semantic eScience Framework
Five Generations of Mediation – Borgman et al. (2008) CyberLearning Report
Cognitive Computing
Realizing the 6th Generation and the Integration of the Other 5!
Schematic of a Cognitive Computing
Archeitecture (courtesy Jim Hendler)
Smart data agents are part of the next
generation of computing infrastructure
mediating research These agents are a fundamental part of the
new cognitive computing platforms being
developed Open-world (versus Closed-world) is essential Linked data will be a fundamental enabler Smart applications!
AGUFM14 – IN23C-3737 (MS Hall A-C)
Framework and relation to external sources
Needed evolution of
cognitive systems
where humans, many
humans are in the
loop – bringing
generations 1, 2 and
3 together with
generations 3, 4, 5
and now 6.
All these generations of mediation are in effect as we conduct research!!
NOTE: INCREASING COMPLEXITY
Smart agents. Open world, semantic agents, with rules… it is notable that these capabilities NEVER made it into the top row of capabilities… in main figure.
Data agents. Ones that can find data for you, and perhaps even convert it to the right format, find contextual information, etc.
Illustration by Roy Pea and Jillian C. Wallis, from C. L. Borgman, H. Abelson, L. Dirks, R. Johnson, K. R. Koedinger, M. C. Linn, C. A. Lynch, D. G. Oblinger, R. D. Pea, K. Salen, M. S. Smith, and A. Szalay, “Fostering Learning in the Networked World: The Cyberlearning Opportunity and Challenge. A 21st Century Agenda for the National Science Foundation. Report of the NSF Task Force on Cyberlearning,” Office of Cyberinfrastructure and Directorate for Education and Human Resources. National Science Foundation, Washington, D.C., 2008.
Mediation Example: S2S Framework and Application
Application Integration
Smart Faceted Browse Dashboards
Linked vocabularies (not Brokering)
Smart Text AgentsSmart Data AgentsRelationship and Assoication RulesCognitive Collaboration
Linked Vocabulary
S2S Application Ontology Figure: While initially developed for CyberLearning, these mediation modes apply more generally
(courtesy Jim Hendler)
Use of Linked vocabularies for
categories of variables,
instruments, and other terms
enables discovery.
MemoryReasoning
Decision Making
Watson, Cogito, and Clarion
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