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Sirris 
Data Innovation Group 
Elena(Tsiporkova(
Who are we? 
Collective centre 
of the technology industry 
 Non profit organisation 
 Industry owned 
MISSION 
Federation...
We have ~2500 member companies 
115 big companies (250 employees) 2485 SME’s (250 employees)
We offer services for 
customized technological innovation 
PRACTICAL ANSWERS 
to tangible technology 
problems 
INNOVATIO...
We operate in 5 domains of technology 
METALS 
COMPOSITES 
PLASTICS  HYBRIDS 
COATINGS 
NANOMATERIALS 
FACTORIES OF THE FU...
Industrial RD project track record 
TWIRL
Online recommendations for the consumer 
domain 
 Innovation goal 
Provide near-real-time recommendations for hyper-local ...
Data requires handcrafted approaches 
 Challenging application context 
 No explicit user ratings 
 Extremely diverse set ...
Paradigm shift to wearable and embedded 
devices 
 Embedded and wearable devices allow to access and manipulate 
diverse i...
while HW/SW design remain important, 
it’s all about data 
algorithms 
trends  patterns 
privacy  
security 
uncovering 
i...
Pro-active decision support in data-intensive 
environments 
 Innovation goal 
Turn an overwhelming amount of data into us...
Emergency dispatching prototype 
Pro-active decision support 
! Aid commanders, superiors,... by 
providing suggestions fo...
User state modeling is still challenging 
 Accurate user state recognition requires 
 real-time user state monitoring 
 ro...
The European Platform to Promote Healthy 
Lifestyle and improve care through a Personal 
Persuasive Assistant 
 Innovation...
User profile modelling 
• lifestyle 
• health 
• personality 
• context
User profile modelling 
• lifestyle 
• health 
• personality 
• context 
• eating  drinking 
• activities 
• frequent loca...
User profile modelling 
• lifestyle 
• health 
• personality 
• context 
• medical history 
• weight 
• body composition 
...
User profile modelling 
• lifestyle 
• health 
• personality 
• context 
• psychological 
• motivational 
• ...
User profile modelling 
• lifestyle 
• health 
• personality 
• context • demographics 
• social 
• professional 
• ...
capture objective  subjective data 
user-friendly questionnaires 
 feedback gathering 
indoor  outdoor 
localisation 
cont...
combine, analyse and enrich data 
• frequent locations 
• most active hours of 
the day 
• ...
Condition monitoring for solar energy production 
 Innovation Goal 
Reduce the maintenance costs of solar plants by means ...
Mediation  querying of 
non-standardized event log data 
through an ontology
Characterization of 
production behavior
Clustering based on 
daily operational dynamics
Data innovation is not … 
 About volume 
 Even relatively small datasets can be very complex to understand and handle 
 Da...
At the core of data innovation are ... 
 The algorithms that interpret data collected from online 
sources, sensors and em...
Agenda 
 13:30 - 14:00 : Introduction to the project 
 14:00 - 15:00 : Project partner presentations 
 15:00 - 15:20 : Use...
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2 partners ed_kickoff_sirris

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Elucidata Kick-off event - Sirris data innovation group

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2 partners ed_kickoff_sirris

  1. 1. Sirris Data Innovation Group Elena(Tsiporkova(
  2. 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. 3. We have ~2500 member companies 115 big companies (250 employees) 2485 SME’s (250 employees)
  4. 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. 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. 6. Industrial RD project track record TWIRL
  7. 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. 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. 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. 10. while HW/SW design remain important, it’s all about data algorithms trends patterns privacy security uncovering insights storage
  11. 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. 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. 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. 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. 15. User profile modelling • lifestyle • health • personality • context
  16. 16. User profile modelling • lifestyle • health • personality • context • eating drinking • activities • frequent locations • ...
  17. 17. User profile modelling • lifestyle • health • personality • context • medical history • weight • body composition • ...
  18. 18. User profile modelling • lifestyle • health • personality • context • psychological • motivational • ...
  19. 19. User profile modelling • lifestyle • health • personality • context • demographics • social • professional • ...
  20. 20. capture objective subjective data user-friendly questionnaires feedback gathering indoor outdoor localisation continuous capturing in the background refining rudimentary activity recognition
  21. 21. combine, analyse and enrich data • frequent locations • most active hours of the day • ...
  22. 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. 23. Mediation querying of non-standardized event log data through an ontology
  24. 24. Characterization of production behavior
  25. 25. Clustering based on daily operational dynamics
  26. 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. 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. 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

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