The document discusses how data analytics is moving beyond just data processing to affect Industry 4.0. It summarizes the research areas and industry partnerships of the Insight Centre for Data Analytics in NUI Galway, including linked data, machine learning, and media analytics. Key applications discussed are monitoring energy consumption using stream processing and event detection, predicting future behavior through machine learning, and detecting and classifying anomalies to inform predictive maintenance decisions.

1. Data analytics beyond data processing
and how it affects Industry 4.0
Mathieu d’Aquin
Vice Director, Insight Centre for Data Analytics, NUI Galway
@mdaquin

2. 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

3. Industry partnerships

4. Data at Web Scale
Interoperability, data
integration
Automated reasoning,
decision making from
streaming data
Data and Knowledge
Infrastructure
Data Semantics

5. Industry 4.0?
1 2 3 4

7. Collaborative information platforms
Knowledge extraction from text
Sentiment analysis, opinion mining
Formalised knowledge sharing
knowledge discovery
autonomous knowledge agents
Modelisation, simulation
Data curation, cataloguing

8. Predictive analytics
Machine learning
Data mining
Stream processing
Event processing
Granular, adaptable privacy and security
Semantic interoperability
Semantic interpretation

9. Example
Monitoring energy consumption from machines / appliances / devices.

10. Example
Monitoring energy consumption from machines / appliances / devices.
StreamProcessing
EventDetection
EventProcessing
DecisionSupport

11. Example
Predicting future behaviour using machine learning.

12. 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

13. Example
Data
curation to
abstract
anomalies
into
common
templates.
...
dataset 1
dataset 2
dataset 3
…
dataset n

14. Example
Data
curation to
abstract
anomalies
into
common
templates.
...
dataset 1
dataset 2
dataset 3
…
dataset n

15. 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
...

16. 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

17. Example
...
Data
annotation,
metadata
Data
annotation,
metadata
Data
annotation,
metadata
Data
annotation,
metadata
...
Data mining
(formal concept
analysis)
Anomalous
Data
Templates

18. Example
...
Data
annotation,
metadata
Data
annotation,
metadata
Data
annotation,
metadata
Data
annotation,
metadata
...
Data mining
(formal concept
analysis)
Anomalous
Data
Templates
Anomaly
signatures

19. 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)

20. 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
typology
ontology

21. 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
typology
ontology
Decision
(predictive
maintenance)

22. 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.

23. 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.

24. insight-centre.org
nuig.insight-centre.org
mdaquin.net
@mdaquin
we are always excited to talk to potential partners, and to new talents.