inLab FIB & Industry 4.0

www.cit.upc.edu

http://inlab.fib.upc.edu@inLabFIB

Director Professor Josep Casanovasjosepk@fib.upc.edu

Ernest Tenienteernest.teniente@upc.edu

inLab FIB UPC is a research & innovation lab of the Barcelona School ofInformatics (FIB) at UPC

It has over 35 years of experience with providing applications & servicesfor public and private institutions

Integrates experts with broad experience (technical and academic staff)with young talent (students)

MISSIONTo transfer knowledge to society through developing humantalent and R&D&i multidisciplinary projects based onbreakthrough ICT technologies, simulation and data science.

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Collaboration with companiesCollaborations (some examples):• Visualization, analysis & optimisation of current and future

scenarios -> Risk reduction• Development of innovative ICT solutions and applications• Technical assessment, training and specialized services in our

expertise areas

Research & Development collaboration models: Open Innovation & Joint Labs, Industrial doctorates, Joint collaboration international (H2020) and national projects, Subcontracting

Sponsorship Programmes (Talent Program)

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Recent partners

See full list at http://inlab.fib.upc.edu/en/col-laboradors

Members of:

R + D Areas of expertiseCombining ICT, data science and

simulation

Modeling, simulation & optimization• Feasibility studies and/or improvements to

systems and processes • Applied to industry 4.0, transport, logistics,

and emergency systems.• Social simulation applied to demography,

population dynamics, epidemiology…• Energy efficiency in buildings and transport

Microscopic simulation of passengers movements in the new terminal of the airport of Barcelona. AENA-INDRA

More information:http://inlab.fib.upc.edu/en/experteses/modelitzacio-simulacio-i-optimitzacio

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Smart MobilityPublic transport systems, trafficmanagement, dynamic Routing applications, traffic and mobility data processing• New generation forecasting models for high

quality traffic and travel information, short-term real-time predictions.

• Traffic data analytics: data filtering, completion and fusion, big data, interoperability, floatingpassenger data.

• New mobility concepts: ridesharing, demand-responsive transportation modes, connected cars.

• Multimodal journey planners, dynamic vehicle routing for fleets.

• Macro, meso and micro traffic simulation.

More information:http://inlab.fib.upc.edu/en/experteses/smart-cities

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Mobile Solutions

• Integration with wearables technology and IoT

• Mobile applications for geoservices based on OpenStreetMap

• Mobile Apps Learning Lab• iOS, Android Apps

development• Leading OpenStreetMap in

Catalonia. More information: https://inlab.fib.upc.edu/en/experteses/aplicacions-mobils-i-gis

ParkFinder - SEAT

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Cybersecurity• Training and cyber security

awareness• Security audits• Forensic analysis• Incident Response• Monitoring of networks• Development of systems for

detecting malware and electronic fraud

• Security of applicationsFirst Spanish Response Team

More information:http://inlab.fib.upc.edu/en/experteses/seguretat-i-infraestructures-tic 10

ICT environments andservices to support learning

• Learning Analytics• Smart learning environments• Information systems for

university management, computer labs

• Systems for measuring and analysing academic results.

More information:http://inlab.fib.upc.edu/en/experteses/entorns-i-serveis-tic-de-suport-laprenentatge-i-la-gestio-universitaria

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Data Science and Big DataSmart data, methods and statistical techniques for analysing and processing data and their interoperability • Data mining• Advanced statistical analysis• Measurement of intangibles

(satisfaction, quality, etc.)• Open data• Integration, fusion and processing

of large volumes of data• Big data architectures• Dashboards , data warehouse, BI

More information:http://inlab.fib.upc.edu/en/experteses/anali

sis-i-tractament-de-dades

Queries and large data matrix analysis for the Centre for Opinion Studies (CEO) of the Government of Catalonia

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Software (service?) engineering

• (Semantic) ontologies• Service and business process

engineering• Semantic integration• Interoperability and

integration of systems• Software as a Service and

interoperability technologies

More information:http://inlab.fib.upc.edu/en/experteses/internet-collaborativa

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System

Several visions of a system

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Industry 4.0 world

• Technology is not a problem• Raw data (in itself) does not have a (huge) value• How do we transform data into knowledge?• How do we achieve a common understanding of the service being provided?

All engineering disciplines are founded on models that are analyzable and can predict the properties of the artifact being engineered

Key problem: have to give an unambiguous, easy to understand account of our understanding of an organization and how it works, also how the new system will fit in that organization

We can do so with English (textual) descriptions; but such descriptions are often cumbersome, incomplete, ambiguous and can lead to misunderstandings

Then, we use ontologies for this purpose, i.e. to describe proposed requirements and designs for the new system

Ontologies capture people’s understanding (conceptualization) of what is being handled

(Semantic) Ontologies

“Quality is never an accident.It is always the result of intelligenteffort”.William A. Foster

“The hardest single part of building a software system is deciding what to build, maintain / check / evolve “Fred Brooks

SistematizationOrganization Communication Analysis Empathy NegotiationConflict resolution ...

Why is this also important?

The idea is not ...

...neither...

RE goals

Features of ontology definition

Criteria

Methodology Tools

People

Specification strategy

Context

Artifacts

How should we do it?

An example in the BIG IoT project

Languages such as UML are based in

first order logic

Only symbols?

Models “speak” in an unambigousway and they can provide a “response” withanalysis tools

Automation capability(analysis, verification, generation...)

Traffic management service: city map

Test-driven Software Development

Ontology-based Data Access

Automated Code Generation

Automated Reasoning

Ontology-based Data Exchange

Visualization of Large Conceptual Schemas, like HL7

Learning Analytics

…

Other advantages of using ontologies

Business Process Modeling• Key activity in organizations

Artifact-centric process modeling• Focus on data

• Contrast to traditional process modeling focused on activities/processes

• Business artifacts updated by services (service engineering)

• BALSA framework: 4 dimensions for artifact-centric models

• Characteristics• Focus on data• Intuitive• Formal• Flexible

Particularly important for providing SaaS Business analysis can be performed from the models

(Artifact-centric) Business Process Modeling

http://inlab.fib.upc.eduinlab@fib.upc.edu+34 93 401 69 41

c/ Jordi Girona 1-3Campus Nord. Edifici B608034 Barcelona

Twitter: @inLabFIB

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