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INNOVANET

Innovation Engineering for the

Support of Commercial / Scientific Discovery

Don Allen (PIRA E-Media)dona@pira.co.uk

Andreas Persidis (Biovista)

IST- 2001-38422

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NEED FOR SYSTEMATIC INNOVATION

• Increasing Competition

• Innovation speed is becoming key

• Competitive advantage based increasingly on knowledge content

• There is an innovation deficit especially in knowledge intensive industries

• Pressure for systematic innovation exists

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CURRENT ISSUES

• AI and ‘expert mode’ operation of systems are not appropriate

• The scientific discovery process needs to be understood

• We need to think about:– Knowledge life cycle– Knowledge value chain

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GOALS OF INNOVANET

• Strategic Roadmap on Systematic Innovation as an aid to R&D investment decision making

• Create a model of systematic innovation based on knowledge life cycle and related principles

• Align technology provider with end user views• Define a high-level specification of an

Innovation Engineering Environment (IEE)• Create an interactive resource that helps design

elements of the IEE

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PROJECT PLAN• Define a systematic innovation model• Perform a Delphi study

– Qualitative questionnaire to finalize model– Quantitative questionnaire to confirm trends

• Bibliometric analysis of EC projects, patents and scientific papers to identify trends in objective manner

• Gap-fit analysis between current and desired state

• Definition of an IEE and other resources• Creation of the Roadmap

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INNOVATION KNOWLEDGE LIFE CYCLE

Community

Knowledge

Working Knowledge

Community Knowledge

Organizational Knowledge

3. Apply knowledge

5. Rate experience

1. Select knowledge resources

4. Gather experience

6. Share experience

2. Focus on relevant knowledge

Innovation Process

Known Problems

Potential Problems

Become aware

select problem

potential side effects

0. Select relevant domain/community

Solved Problems

contribution

Community

Knowledge

Community Knowledge

Anomalous state of knowledge

Knowledge

Cycle

Problem Cycle

CyKnowledge Cycle

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PHASES OF INNOVATION

• Problem Identification

• Ideation

• Approach Development

• Operationalisation

• Evaluation

• Exploitation

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KNOWLEDGE ACTIVITIES

• Selection of communities/domains

• Selection of knowledge sources

• Focus on relevant knowledge

• Apply knowledge

• Gather experience

• Rate and share experience

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GENERIC KNOWLEDGE OBJECT MANIPULATION FUNCTIONALITIES

• Intelligent representation

• Match-making

• Discovery

• Management of Knowledge Objects

• Interaction / Communication

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SERVICES (1)

• Intelligent representation– Editing, Visualization, Alignment, Updating,

Packaging, Versioning

• Match-making– Query support, semantic matching,

personalization, brokering

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SERVICES (2)

• Discovery– Trends analysis, applications discovery,

needs discovery, aggregates discovery

• Management of Knowledge Objects– Version control, comparables, terms of use,

person/community profiling

• Interaction / Communication– Thread comparison, emergence, thread

content summarization

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FINDINGS (1)

• Systematic Innovation (SI) is a controversial concept (considered essential by some and impossible/undesirable by others)

• Tools for SI are currently not available in an integrated and usable form

• The concepts of knowledge life cycle and knowledge value chain are central to SI

• Soft (human-centric) issues which are important to SI are currently not well covered

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FINDINGS (2)• Scientific discovery principles need to be better modeled

and tools to implement them should be created• Applications and Needs discovery are two steps that

bridge the gap between products and needs that should be supported and can lead to SI

• In the next 3-5 years software R&D funding should focus on a number of areas including the following:– Flexible representation schemes to cover updating, versioning,

packaging and alignment of knowledge– Information resource alignment and interoperability– New Reasoning algorithms to provide synthesis and overview of

complex data– Interface and visualisation techniques– Modeling and simulation– Planning and service integration

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INNOVANET CONSORTIUM

INMARK Estudios y Estrategias S.A.

Biovista

FhG-IPSI

VUB STARLab, Department of Computer Science    

Istituto Trentino di Cultura - Istituto per la Ricerca Scientifica e Tecnologica

PIRA

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