ontologies in the context of cloud computingvfinal · ontology for big systems data creators and...

Ontologies in the Context of Ontologies in the Context of Ontologies in the Context of Ontologies in the Context of

Cloud Computing and Big DataCloud Computing and Big DataCloud Computing and Big DataCloud Computing and Big Data

Ontologies and Conceptual Models for

Industrial Enterprises Group

INGAR (CONICET – UTN)

INTEC (CONICET – UNL)

Santa Fe - Argentina

Nowadays ContextNowadays ContextNowadays ContextNowadays Context

Product customization

Short Product Lifecycles

with extremely short

time-to-markets

Global businesses

entered a new era of

decision making in which

the ability to gather,

store, access, and

analyze enormous

amounts of data has

grown exponentially

Product development

processes & manufacturing

operations are distributed

over the globe

The success of global manufacturing enterprises depends upon

the entire worldwide integration of many stakeholders

working on collaborative decision-making processes

Collaborative Manufacturing

Collaborative Supply Chain

Requires synchronization across a broad scope of manufacturing

activities performed by multiple organizations that generate and

exchange enormous amounts of heterogeneous data (different

syntaxes, distinct semantics, various granularities and time-frames)

Collaborative Environment

Collaborative ManufacturingCollaborative ManufacturingCollaborative ManufacturingCollaborative Manufacturing

Suppliers RetailersDistribution

CentersCustomers

ProducersDeposits &

WarehousesProducers

MakeSource Deliver

SourceMake

Deliver

Collaborative business process

Collaborative business process

Many companies (suppliers’ suppliers, manufacturers, clients, 3PLS, 4PLs, etc.)

aligned into collaborative business process.

Globally integrated versus linear and silo-oriented SCs.

Collaborative Supply ChainsCollaborative Supply ChainsCollaborative Supply ChainsCollaborative Supply Chains

55

Design, planify and

operate

MakeSource Deliver

Enterprise CustomerSupplierSupplier’s

Supplier

Final

CustomerDesign, planify

and operate

Design, planify and

operate

DataData

Horizontal integration:Same layer applications

Vertical integration:

Different time scales

and information

granularities

Collaborative Supply ChainsCollaborative Supply ChainsCollaborative Supply ChainsCollaborative Supply Chains

Smart Manufacturing according to the SMLC Group (“Smart Manufacturing Leadership Coalition”)

CEP Journal,

October 2012

Smart ManufacturingSmart ManufacturingSmart ManufacturingSmart Manufacturing

Factories of the FutureFactories of the FutureFactories of the FutureFactories of the Future

Cloud-based platforms in high

tech manufacturing

Cloud-based marketing automation applications

to plan, execute and track results of every

campaign

Cloud-based Human Resource Management(HRM) systems to unify

all manufacturing locations globally

Use of cloud-based platforms to capture

knowledge and manage rules

Factories of the FutureFactories of the FutureFactories of the FutureFactories of the Future

SW

CW PW

Cloud efficient supply – Service World

Cloud-based engineering – Computational World

Cloud-based automation – Physical World

Manufacturers rely on cloud-based systems to streamline key areas of

their business: marketing, design, manufacture, automation, supply, etc.

Rolf Riemenscheider, European Commission, APMS 2012

Nowadays ContextNowadays ContextNowadays ContextNowadays Context

Design, planify and

operate

MakeSource Deliver

Enterprise CustomerSupplierSupplier’s

Supplier

Final

CustomerDesign, planify

and operate

Design, planify and

operate

Collaborative Supply Chain and Collaborative Manufacturing lead to complex

systems that resort to Cloud Computing and Big Systems technologies

Why OOOOntologiesntologiesntologiesntologies in the context

of Cloud Computing and Big

Systems?

• Can we continue designing cloud-based data services in

the same way that we do in traditional systems?

• How to articulate huge amounts of data (with different

syntaxes and semantics) of several partners that need

to collaborate?

• How to provide structure to unstructured data?

• ….?

An annual series of events that involves the ontology community and

communities related to each year's theme chosen for the summit.

Ontolog,

NIST (US National Institute of Standards and Technology)

NCOR (US National Center for Ontological Research ),

NCBO (National Center for Biomedical Ontology)

IAOA (International Association for Ontology and its Applications)

NCO_NITRD (National Coordination office for the Networking and

Information Technology Research and Development)

Co-organized

by:

Activities:

Main Deliverable: Ontology Summit 201x communiqué

2-day face-to-face symposium

3 months of

virtual discourse (over our archived mailing lists) and

virtual panel sessions (over augmented conference calls)

Ontology Summit Ontology Summit Ontology Summit Ontology Summit SeriesSeriesSeriesSeries

Ontology Summit Ontology Summit Ontology Summit Ontology Summit SeriesSeriesSeriesSeries

Ontology Summit 2012 explored the current and potential uses of

ontology, its methods and paradigms, in big systems and big data.

Ontology for Big Systems - 2012

Organization:

• Big systems engineering

• Big data challenge

• Large scale domain applications

• Cross track 1 – Quality

• Cross track 2 – Federation and integration of systems

What can ontology provide to support and understand

Big Systems?

How does ontology provide that?

How does the science and engineering of Big Systems

impact ontology?

Key

questions

Big data, complex techno-socio-economic

systems, intelligent or smart systems, cloud

computing, and collective intelligence:

Ontology Summit Ontology Summit Ontology Summit Ontology Summit 2012201220122012Ontology for Big Systems

Big systems engineering

They serve as the ground truth for design and analysis

in that they are the authoritative source of information.

Engineers have

always built

models

to specify products to be built,

to describe physical systems,

to describe system interaction

with the world.

They carry an (often implicit) ontology, expressing a theory or a set of

assumptions, about the world or some part of it.

Modeling languages need a precise meaning to enable collaboration,

standards, and reasoning. This is where ontology comes in

Ontology for Big Systems

Data creators and publishers need to make explicit what their

data represents together with the context of the data and its

creation.

Big Data Applications

intelligently combine it with other data sets

understand the datagarner information and knowledge from it,

We need to be able to represent the assumptions and

conceptualizations that underpin knowledge in those domains.

To efficiently do this

Ontology Summit Ontology Summit Ontology Summit Ontology Summit 2012201220122012

Ontology Summit Ontology Summit Ontology Summit Ontology Summit 2012201220122012Ontology for Big Systems

Ontologies and ontological analysis are vital parts of a solution

addressing the problems of architecting and engineering big systems

and big data.

Ontologies can be used to:

• Make explicit and accessible the vital assumptions about the nature

and structure of engineered systems and their components.

• Help people better understand and disentangle the complexity of big

engineered systems and their social, economic and natural

environment

• Enable integration among systems and data through semantic

interoperability.

Ontology for Big Systems

• Improve models and modeling, their adaptability and reuse, and resulting

design.

• Enhance decision-support systems.

• Aid in knowledge management and discovery.

• Provide a basis for more adaptable systems

Ontologies can be used to: (cont.)

Ontology Summit Ontology Summit Ontology Summit Ontology Summit 2012201220122012

More details in:

http://ontolog.cim3.net/OntologySummit/2012/communique.html

Group Research ExperienceGroup Research ExperienceGroup Research ExperienceGroup Research Experience

Development of conceptual models and domain

ontologies in the fields of industrial enterprises and

software development processes:

• Supply Chain ONTOlogy (SCOnto)

• PRoduct ONTOlogy (PROnto)

• Scheduling Ontology (SchedOnto)

• Collaborative Model for capturing and representing the engineering Design process (CoMoDe)

Thank you very much for your Thank you very much for your Thank you very much for your Thank you very much for your

kind attention!!kind attention!!kind attention!!kind attention!!

Silvio Gonnet� [email protected]

Gabriela Henning � [email protected]

Horacio Leone� [email protected]

Luciana Roldán� [email protected]

Marcela Vegetti� [email protected]

Questions?

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