Information Model Driven Semantic Framework Architecture and Design for

Distributed Data Repositories

AGU 2011, IN51D-04

December 9, 2011Peter Fox (RPI) pfox@cs.rpi.eduTetherless World Constellation. Thanks to the SESF team: Patrick West,Eric Rozell, Stephan Zednik, Han Wang, Rajashree Deka, Linyun Fu,Deborah McGuinness and Jim Hendler …

There are many words…

• Information models• Why frameworks and not systems?• Architecture and design factors• The distributed part of it..• Bringing it all together - framework

semantics – integration and configuration• Some models• A role for participation

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Data-Information-Knowledge Ecosystem

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Data Information Knowledge

Producers Consumers

Context

PresentationOrganization

IntegrationConversation

CreationGathering

Experience

Curators

Prior to 2005, we built systems

• Rough definitions– Systems have very well-define entry and

exit points. A user tends to know when they are using one. Options for extensions are limited and usually require engineering

– Frameworks have many entry and use points. A user often does not know when they are using one. Extension points are part of the design

– Platforms are built on frameworks

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Architecture

• Definition: – “is the art of expressing a model or concept

of information used in activities that require explicit details of complex systems” (wikipedia)

– “… I mean architect as in the creating of systemic, structural, and orderly principles to make something work - the thoughtful making of either artifact, or idea, or policy that informs because it is clear.” Wuman

• “Information architecture is the categorization of information into a coherent structure, preferably one that the most people can understand quickly, if not inherently.

• It's usually hierarchical, but can have other structures, such as concentric or even chaotic.” (wikipedia)

Design… important?

• Factors: primary are 1) web and 2) object

• Why so obsessed with information and not knowledge, or data?

• It’s about the humans, as mediators of the knowledge (and data…)

Information modeling

• Conceptual models, sometimes called domain models, are typically used to explore domain concepts and often created – as part of initial requirements envisioning efforts

as they are used to explore the high-level static business or science or medicine structures and concepts

– as the precursor to logical models or as alternatives to them

• Followed by logical and physical models

• Introduced in ANSI processes in 1978!

• A logical model is provable in the mathematics of data science. E.g. for relational databases, logical models generally conform to relational theory.

• Thus a logical model contains only fully normalized entities. Some of these may represent logical domains rather than, for e.g. potential physical tables.

Art or skill?

• Form follows function (Sullivan) – this was put into effect in building structures, e.g. homes?

• Based on information theory and semiotics, it should be clear that the answer (art/skill?) is ‘yes’ (both)

• Now, remember this.. 10

Summary

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Summary

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“A” framework

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IMs –> form and function

Application integration semantics

Service: an OpenSearch-described resource that supports S2S queries Query: a GET or POST

service with parameters defined as S2S parameters

Parameter: an input to S2Squeries for requesting specific results

Widget: a user interface element that visualizes results of S2S queries and facilitates user input (i.e., parameter selection)

Product Quality IM

Information models evolve ontologies

• VSTO -> Solar-Terrestrial Obs. & Meas. (ISO based)

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Participation

• We have many information models (most make it into ontologies)

• They are an excellent means to integrate domain ‘knowledge’ … ooopppsss … information

• Allow discourse and consensus development • Ours are scoped using use cases

• This means … you may be asked to participate• Please do!

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Further Information

• http://tw.rpi.edu/web/project/SESF

• NSF/OCI STCI 0943761 (ARRA)

• Contacts: – pfox@cs.rpi.edu, @taswegian– westp@rpi.edu– rozele@rpi.edu – zednis@rpi.edu– dlm@cs.rpi.edu– hendler@cs.rpi.edu

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