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.


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


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


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


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

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