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Algorithmic Data Analysis

Algorithmic Data Analysis. Hannu Toivonen. Mission. To develop useful algorithmic data analysis methods for other sciences and for industry. Basic research in computer science and applied work on problems arising from applications. Data analysis.

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Algorithmic Data Analysis

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  1. Algorithmic Data Analysis Hannu Toivonen

  2. Mission • To develop useful algorithmic data analysis methods for other sciences and for industry. • Basic research in computer science and applied work on problems arising from applications

  3. Data analysis • Data analysis is becoming more important in other sciences and in industry • New measurement methods • Ability to store data • High-dimensional large data sets • Non-traditional forms (e.g., strings, trees, graphs) • Data analysis lags behind

  4. Data mining • Has emerged as a major research area in the interface of computer science and statistics • Machine learning, databases, algorithms • Data analysis questions are increasingly visible in database and algorithms research • Theory and practice interact • Fits very well within the overall mission of HIIT • Basic research in computer science • Fast applicability, possibility of impact

  5. Goals • Develop novel data analysis techniques for the use of other sciences and industry • How? • Look at data analysis problems arising in practice • Abstract new computational concepts from them • Analyse the concepts and develops new computational methods • Take the results into practice • Theoretical work in algorithms and foundations of data analysis can have fast impact in the application areas • The applications feed interesting novel questions to theoretical research

  6. Groups in the ADA programme • Combinatorial pattern matching (Esko Ukkonen, Kjell Lemström, Juha Kärkkäinen, Juho Rousu) • Pattern and link discovery (Hannu Toivonen) • Parsimonious modelling (Jaakko Hollmen) • Adaptive computing (Patrik Floréen) • Statistical machine learning and bioinformatics (Samuel Kaski) • Data mining: theory and applications (Heikki Mannila)

  7. Status • Very good groups in several themes • National Centers of Excellence in research etc. • Cooperation between groups • Excellent network of collaborators in other sciences • Strong international aspect

  8. Vision 2009 • Continue to be world-leading in several themes • Strong interaction with departments in TKK, UH, and other parts of HIIT • Strong interaction with industry and other sciences • About current size

  9. Example: ContextPhone • ContextPhone (IEEE Pervasive Computing 2005) • a prototyping platform for mobile and contextual applications • context data collection, distribution, display • the first open source toolbox for standard mobile phones (Nokia S60) • Academy project 2002-5

  10. ContextPhone

  11. ContextPhone

  12. ContextPhone

  13. ContextPhone

  14. ContextPhone • Example applications: presence-augmented phonebook, automatic media annotation and distribution, logger, … • Research into data analysis algorithms, privacy issues, social networks, social awareness, human computer interaction, arts, … • Has spawned research collaboration and projects across HIIT as well as internationally • Commercial product/service: Jaiku Ltd, www.jaiku.com

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