This document summarizes information about several data innovation projects from Sirris, a non-profit technology organization. It describes projects involving online recommendations, wearable devices, emergency dispatching, healthy lifestyle coaching, and solar energy production monitoring. For each project, it outlines the innovation goal, technical approaches used, and remaining challenges. Throughout, it emphasizes that data innovation requires domain expertise, customized algorithms, and access to relevant data.

1. Sirris
Data Innovation Group
Elena(Tsiporkova(

2. Who are we?
Collective centre
of the technology industry
Non profit organisation
Industry owned
MISSION
Federation
for the technology industry
“To increase the competitiveness of companies of the Agoria
sectors through technological innovations”

3. We have ~2500 member companies
115 big companies (250 employees) 2485 SME’s (250 employees)

4. We offer services for
customized technological innovation
PRACTICAL ANSWERS
to tangible technology
problems
INNOVATION PROJECTS
realisation from idea
to product
TECHNOLOGICAL TRENDS
ONLINE LIBRARY
NEWSLETTERS
THEMATIC SEMINARS
workshops
RESEARCH DEVELOPMENT
with industrial partners
ACCESS TO HIGH-TECH
infrastructure
INDIVIDUAL SUPPORT
SOURCE OF INSPIRATION
SHARED RISKS
SERVICES

5. We operate in 5 domains of technology
METALS
COMPOSITES
PLASTICS HYBRIDS
COATINGS
NANOMATERIALS
FACTORIES OF THE FUTURE
WORLD CLASS TECHNOLOGIES
ADDITIVE MANUFACTURING
ECO-MECHATRONICS
SENSORIZED FUTURE
MODEL BASED DESIGN
SOFTWARE ENGINEERING
CLOUD COMPUTING
DATA INNOVATION

6. Industrial RD project track record
TWIRL

7. Online recommendations for the consumer
domain
Innovation goal
Provide near-real-time recommendations for hyper-local
hyper-personal content e.g. concerts, markets, restaurants, local
events happenings, …
EVENTS
RESTAURANTS
DEALS
TWIRL

8. Data requires handcrafted approaches
Challenging application context
No explicit user ratings
Extremely diverse set of items - low
coverage
Noisy data - item profiling is difficult
Standard recommendation approach
not applicable
Collaborative filtering - needs “coverage”
Content-based - needs item profiling
Hybrid recommendation strategy
developed
Customized collaborative filtering
content–based algorithms

9. Paradigm shift to wearable and embedded
devices
Embedded and wearable devices allow to access and manipulate
diverse information about people and their environment
Devices carried by humans or worn in/on the human body as smart phones,
heart rate meters, Google Glass, ...
Sensors integrated into “dumb” objects, such as textiles, toothbrushes,
mattresses, mirrors, thermostats, …

10. while HW/SW design remain important,
it’s all about data
algorithms
trends patterns
privacy
security
uncovering
insights
storage

11. Pro-active decision support in data-intensive
environments
Innovation goal
Turn an overwhelming amount of data into user- and
context-sensitive information to increase situational
awareness and improve decision making
capture, model and fuse raw data about users their
environment from heterogeneous data sources
analyse, filter pro-actively deliver relevant information
using appropriate modalities
h9p://www.astuteproject.eu/(

12. Emergency dispatching prototype
Pro-active decision support
! Aid commanders, superiors,... by
providing suggestions for actions
! Show relevant data only
Server
Multi-modal communication
! Integrated audio, video, voice,
haptic, messaging, map
annotations,…
Adaptive map-centric HMI
! Embedded on-sleeve, in-vehicle, etc.
! Updated in real-time with tracking
feeds, annotations,...
Allbasedonuser
stateandcontext

13. User state modeling is still challenging
Accurate user state recognition requires
real-time user state monitoring
robust user state models
Real-time continuous data capturing of user state is not fully
feasible
still obtrusive and stigmatizing
accuracy vs. low energy (battery) consumption
scalable data storage and transfer
User state modeling is extremely data-intensive and requires
availability of high quality historical data
strategies for model personalization
domain-specific user state interpretation

14. The European Platform to Promote Healthy
Lifestyle and improve care through a Personal
Persuasive Assistant
Innovation goal
Build a digital coaching platform that continuously
monitors, advises and interacts with the user to help
him/her acquire and maintain a healthier lifestyle
h9p://www.withmeproject.eu/(

15. User profile modelling
• lifestyle
• health
• personality
• context

16. User profile modelling
• lifestyle
• health
• personality
• context
• eating drinking
• activities
• frequent locations
• ...

17. User profile modelling
• lifestyle
• health
• personality
• context
• medical history
• weight
• body composition
• ...

18. User profile modelling
• lifestyle
• health
• personality
• context
• psychological
• motivational
• ...

19. User profile modelling
• lifestyle
• health
• personality
• context • demographics
• social
• professional
• ...

20. capture objective subjective data
user-friendly questionnaires
feedback gathering
indoor outdoor
localisation
continuous capturing in
the background
refining rudimentary
activity recognition

21. combine, analyse and enrich data
• frequent locations
• most active hours of
the day
• ...

22. Condition monitoring for solar energy production
Innovation Goal
Reduce the maintenance costs of solar plants by means of real-time
failure prediction operation anomaly detection through
pattern detection in historical data
h9p://www.arrowhead.eu/(

23. Mediation querying of
non-standardized event log data
through an ontology

24. Characterization of
production behavior

25. Clustering based on
daily operational dynamics

26. Data innovation is not …
About volume
Even relatively small datasets can be very complex to understand and handle
Data innovations can already be realized with relatively limited datasets
Having access to the right data with the right quality is essential
About installing configuring platforms, libraries or tools
It’s about statistics, machine learning, experimentation, trial error, ...
Just data crunching
Even the most intelligent algorithm will be useless without some prior knowledge of
the application domain
Gaining initial understanding of the nature, type and quality of data under study is a
must
Ideally the data scientists need to be involved already during the design of the data
capturing phase
Easily scalable in terms of effort and human resources
Few data problems can be solved with off-the-shelf products and IT generalists
Most data problems require a handcrafted “artisanal” approach and data scientists
with deep expertise in statistics, machine learning, …

27. At the core of data innovation are ...
The algorithms that interpret data collected from online
sources, sensors and embedded devices e.g.
data integration from heterogeneous sources
real time information processing and event recognition
inference of additional knowledge from data
The availability of the right amount of the right data with the right
quality
The presence of skilled professionals that combine deep mathematical
skills with practical domain knowledge to exploit data in creative and
innovative ways

28. Agenda
13:30 - 14:00 : Introduction to the project
14:00 - 15:00 : Project partner presentations
15:00 - 15:20 : User group involvement
15:20 - 15:45 : Break
15:45 - 16:45 : Brainstorm on potential demonstrators
16:45 - 17:00 : Next steps closing remarks
17:00 - … : Networking drink