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Interactive Visualization Using vision to think Luc Girardin Macrofocus GmbH.

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Presentation on theme: "Interactive Visualization Using vision to think Luc Girardin Macrofocus GmbH."— Presentation transcript:

1 Interactive Visualization Using vision to think Luc Girardin Macrofocus GmbH

2 Interactive Visualization2 (31) Overview  Introduction  An example: Economic Research  Interactive Visualization  Another example: Investment Funds  Summary

3 Interactive Visualization3 (31) What is the problem?  Amounts of complex data growing faster than capability to analyze it  We know how to collect data and build big data warehouses but… We are lost in this space  Data  Information  Knowledge

4 Interactive Visualization4 (31) Example: Economic research  Survey of prices and earnings in 60 cities around the world  Each city characterized by 40+ different attributes

5 Interactive Visualization5 (31)

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8 Interactive Visualization8 (31) City’O’Scope Demo

9 Interactive Visualization9 (31) Interactive visualization  Use perceptual skills  Provide an overview of global relationships  Frame of reference to embed fine grained tasks  Overview – Zoom in – Details on demand  Encourage exploration and comparison  Reveal the data at several levels of detail

10 Interactive Visualization10 (31) Our approach  Integrated systems  Preserve context  Different views  Tightly linked  Highly interactive

11 Interactive Visualization11 (31)

12 Interactive Visualization12 (31)

13 Interactive Visualization13 (31)

14 Interactive Visualization14 (31)

15 Interactive Visualization15 (31)

16 Interactive Visualization16 (31)

17 Interactive Visualization17 (31)

18 - Is this an outlier? - How do these groups relate to each other? - Are there groups of similar objects? - What is this object? - Why is it an outlier? - If I change this parameter a little bit,. how will the result be affected?

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20 Interactive Visualization20 (31) A different approach…

21 Interactive Visualization21 (31) Fund’O’Scope Demo

22 Interactive Visualization22 (31) Spring-based layout algorithm

23 Interactive Visualization23 (31) i-lists beforeafter

24 Interactive Visualization24 (31) before after  -blended parallel coordinates

25 Interactive Visualization25 (31) What did we learn?  Not enough to connect database to the Web  Tools that fit the way we think and work Easier and faster data access Better understanding of information space Discover new relationships, anomalies

26 www.macrofocus.com E-mail: info@macrofocus.com

27 Interactive Visualization27 (31) Nanotechnology

28 Interactive Visualization28 (31) From the lab…

29 Interactive Visualization29 (31) …to the Web

30 Interactive Visualization30 (31) Atom’O’Scope Demo


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