TIVIT Interactive: D2I: Research Challenges
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Transcript of TIVIT Interactive: D2I: Research Challenges
Finnish Strategic Centres for Science, Technology and Innova8on Informa8on and Communica8on Industry and Services
Data to Intelligence (D2I): Research Challenges A research programme on data-‐driven intelligent services
Petri Myllymäki December 14, 2011 14.12.2011
D2I Vision & Mission
• Vision 2015/2016 We have developed the necessary intelligent methods and tools for managing, refining and u8lizing diverse data sources. The results enable innova8ve business models and services.
• Mission To boost the Finnish interna8onal compe88veness through intelligent (context-‐sensi8ve, personalized, proac8ve) data processing technologies and services that add measurable value.
14.12.2011 Tivit Interac8ve, Dec 14, 2011
WP OS: Organisations, Services e.g. business models, processes, user-‐centric requirements
WP DT: Data, Technologies e.g. semantics, structures,
platforms, security WP MA: Methods, Algorithms e.g. models, analytics, data mining and understanding
Intelligent data-‐driven systems
Main steps: 1. Iden8fy the context
– Context: both the environment and the user (profile, preferences, status, cogni8ve state)
– Both the current and also the future context – Context inferred automa8cally
2. Retrieve relevant informa8on – Relevancy defined with respect to the context and the user
3. Present the informa8on to the user – In an understandable form that supports informed decision-‐making – Gather (explicit and/or implicit) feedback and go back to Step 1.
14.12.2011 Tivit Interac8ve, Dec 14, 2011
PROACTIVE
CONTEXT-‐SENSITIVE
PERSONALIZED
ADAPTIVE
Main technological challenges • Data is big
– Wikipedia: “Big data are datasets that grow so large that they become awkward to work with using on-‐hand database management tools”
– Need (predic8ve) models for big data analy8cs (machine learning, data mining, data analysis,…)
• Data is heterogeneous and unstructured – Wikipedia: “Data sets also grow in size because they are
increasingly being gathered by ubiquitous informa8on-‐sensing mobile devices, so[ware logs, cameras, microphones, RFID readers, wireless sensor networks and so on”
– Need data fusion methods for integra8ng heterogeneous and parceled data sources
• Data is complex – Data elements are not only numerous, they are o[en broad
(consis8ng of many measurements), so that making sense of data is difficult
– Need sophis8cated data visualiza8on and summariza8on methods that support informed decision-‐making
14.12.2011 Tivit Interac8ve, Dec 14, 2011
Can we do it?
• Intelligent (adap8ve, context-‐sensi8ve, personalized, proac8ve) systems require sophis8cated models, algorithms and tools
• Luckily, Finland is an interna8onally recognized leader in many of the relevant research fields, see e.g. the Evalua8on of Computer Science Research in Finland 2000-‐2006 (Academy of Finland, 8/07): “machine learning and probabilis1c methods are arguably the strongest single area of computer science in Finland”
• However, the challenges posed by big data require in many areas new methodological innova8ons, and more work
• The main challenge is s8ll to bridge the gap between the technology and the business
14.12.2011 Tivit Interac8ve, Dec 14, 2011
D2I Contacts
• Tivit Oy – Pauli Kuosmanen, CTO – [email protected] – www.8vit.fi
• Focus Area Director (FAD) – Jukka Ah8kari, Development Director – Logica – [email protected] – www.logica.fi
• Academic Coordinator (AC) – Petri Myllymäki, Ph.D., Professor – Department of Computer Science, University of Helsinki – [email protected] – www.hiit.fi
14.12.2011 Tivit Interac8ve, Dec 14, 2011
Thank you!
More informaSon: D2I FAD: Jukka.AhSkari @logica.com
D2I AC: Petri.Myllymaki @hiit.fi
hYp://www.datatointelligence.fi/
Petri Myllymäki 14.12.2011