data analytics beyond data processing and how it affects industry 4.0

Data analytics beyond data processingand how it affects Industry 4.0Mathieu d’AquinVice Director, Insight Centre for Data Analytics, NUI Galway@mdaquin

Insight Centre for Data Analytics:- SFI-funded research centre- 5 department, in 4 universities- More than 40 partners- More than 400 researchers

Key areas of research:- Linked Data, Semantic Web

technologies, Web of Data, Open Data- Machine Learning & Statistics, Data

Mining, Knowledge Discovery- Media Analytics- Optimisation & Decision Analytics- Personal Sensing- Recommender Systems

Industry partnerships

Data at Web Scale

Interoperability, data integration

Automated reasoning, decision making from

streaming data

Data and Knowledge Infrastructure

Data Semantics

Industry 4.0?

1 2 3 4

Collaborative information platformsKnowledge extraction from text

Sentiment analysis, opinion miningFormalised knowledge sharing

knowledge discoveryautonomous knowledge agents

Modelisation, simulationData curation, cataloguing

Predictive analyticsMachine learning

Data miningStream processingEvent processing

Granular, adaptable privacy and securitySemantic interoperabilitySemantic interpretation

Example

Monitoring energy consumption from machines / appliances / devices.

Example

Monitoring energy consumption from machines / appliances / devices.

Stream

Processing

Event D

etection

Event P

rocessing

Decision S

upport

Example

Predicting future behaviour using machine learning.

Example

Detecting anomalous behaviour through derivation from the predicted behaviour.

Punctual anomaly (e.g. door

open)no alert

Back to normal (model and real values match)

Consistent anomaly (e.g. malfunction)

alert

Example

Data curation to

abstract anomalies

into common

templates....

dataset 1

dataset 2

dataset 3

…

dataset n

Example

Data curation to

abstract anomalies

into common

templates....

dataset 1

dataset 2

dataset 3

…

dataset n

Example

Data curation to

abstract anomalies

into common

templates....

dataset 1

dataset 2

dataset 3

…

dataset n

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

...

Example

Data curation to

abstract anomalies

into common

templates....

dataset 1

dataset 2

dataset 3

…

dataset n

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

...Data mining

(formal concept analysis)

Anomalous Data

Templates

Example

...

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

...

Data mining (formal concept

analysis)

Anomalous Data

Templates

Example

...

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

...

Data mining (formal concept

analysis)

Anomalous Data

Templates

Anomaly signatures

Example

...

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

...

Data mining (formal concept

analysis)

Anomalous Data

Templates

Anomaly signatures

Engineering & maintenance

reports (text)

Example

...

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

...

Data mining (formal concept

analysis)

Anomalous Data

Templates

Anomaly signatures

Engineering & maintenance

reports (text)

Semantic classificat

ion

Anomaly typologyontology

Example

...

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

Data annotation, metadata

...

Data mining (formal concept

analysis)

Anomalous Data

Templates

Anomaly signatures

Engineering & maintenance

reports (text)

Semantic classificat

ion

Anomaly typologyontology

Decision(predictive

maintenance)

Example

Uses stream processing, event processing, decision support, machine learning, data mining, knowledge discovery, data curation, data cataloguing, semantic annotation, reasoning, data profiling, text analytics…

… from various projects in various domains.

Conclusion

Data analytics and the meaningful, semantic interpretation of data from diverse sources is critical to manufacturing and all other branches of engineering.

Insight(@NUIG) research and develop the key technologies to make smart approaches exploitable by industry, from large scale tech. companies, to innovative SMEs.

insight-centre.org

nuig.insight-centre.org

mdaquin.net

@mdaquin

we are always excited to talk to potential partners, and to new talents.