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Data Visualisation

HCI 0283 Lecture 5 Dynamic Exploration. Data Visualisation. Real Problems. Real world problems are very seldom precisely specified

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Data Visualisation

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  1. HCI 0283 Lecture 5 Dynamic Exploration Data Visualisation

  2. Real Problems • Real world problems are very seldom precisely specified • We want a house for under about £100,000 with at least three bedrooms (although four would be nice), but it’s got to be close to a good school and it would be quite nice if it was near Granny so she could baby-sit for us • Many other problems are specified just as imprecisely • The reason for this is a lack of knowledge • The house-hunter has no idea of • What houses are currently on the market • Where the local schools are • The general price trends • Local variations

  3. Real Problems • The first task is therefore to help the house-hunter understand the relevant data and any hidden relationship within it • The next task is to help the house-hunter solve his problem (finding a house) • During this process the house-hunter may change his requirements • It looks like we might be able to afford a 5-bedroom house if we rent out a couple of rooms • Formulating the problem is as important as finding a solution

  4. Command Line Queries • Conventional databases require queries to be entered in very specific manners • SELECT house-address FROM mydatabase WHERE price < 100,000 AND bedrooms >=3 • The result of this might well be • 0 records returned • 1433 records returned • Neither of these is particularly useful

  5. Command Line Queries • There are many problems with conventional database query languages • Learning a new language • Errors are not tolerated • Too few or too many records returned • No suggestions how to reform a query • Slow! • No context data • Difficult to build a cognitive map of the data

  6. Command Line Queries • The assumption behind command line queries is that the user knows precisely what question he wishes to ask • In fact this is very rarely the case • Dynamic exploration allows the user to continually reformulate his question in light of the information presented to him.

  7. Dynamic Queries • We could replace the command line query with a dynamic display Price Bedrooms Travel time

  8. Dynamic Queries • This makes the task a ‘what if…?’ activity • He is able to explore the data more easily by restating the question in more general terms • Given a collection of objects, each described by the values associated with a set of attributes, find the most acceptable such object or, perhaps, a small number of candidate objects worthy of more detailed consideration • It no longer matter what you are looking for, the important thing is that you can explore the data through a graphical interface

  9. Dynamic Queries • A similar type of task is the selection of a film to watch on DVD • In FilmFinder each film is represented by a coloured square, the colour representing the genre (SF, horror, action, comedy etc) • Alphasliders are used to select subsets of films based on dates, actors, directors or other attributes

  10. Alphasliders • Alphasliders allow users to scan rapidly through and select items from list of alphabetical data Text output Slider thumb Slider area Hunt for Red October A B C D E F G H I J L M N O R S T W Z Index

  11. Attribute Explorer • A drawback to the dynamic queries approach is that data is only disposed when it satisfies the query • There are situations where the display of all data is useful to provide contextural information • This extra information may provide useful hints for exploration • In lecture 3 we showed that data in a histogram can be viewed interactively using sliders • This approach is extended using IBM’s Attribute Explorer to present data in a highly dynamic manner • Suppose we have car data for city MPG, highway MPG, engine size and horsepower

  12. Attribute Explorer

  13. Attribute Explorer

  14. Attribute Explorer

  15. Summary Information • It is not easy to display summary information for a histogram • One way is to show the mean value with a circle • The circles on each histogram move in response to each other

  16. Boolean Operations • Attribute Explorer allows the user to identify those cars which satisfy limits on city MPG AND highway MPG AND horsepower AND engine size • This is clearly a Boolean operation • The InfoCrystal allows a number of different Boolean operations to be performed on a dataset

  17. InfoCrystal

  18. Link Crystal • This approach is used as the Link Crystal within Attribute Explorer • E.g. an link crystal showing house price, number of bedrooms and garden size Price Bedrooms Garden size

  19. Fuzzy Queries • Mathematical and computing techniques exist to handle fuzzy logic • These may be useful, but are generally designed to solve specific optimisation problems • Our selection problem is not very specific! • The best fuzzy computer is still the human brain

  20. Coffee Time!

  21. Very Large Databases • The only limit to the number of attributes that can be handled by Attribute Explorer is the size of the display screen • Hypervariate data is always a problem • One technique is used in the VisDB tool • This uses vertical scales to select the attribute limits and allows the importance of the attributes to be weighted

  22. Very Large Databases • The objects are now ordered depending upon how well then satisfy the attribute values selected • The data is then arranged in a spiral pattern

  23. Neighbourhood Explorer • Neither Attribute Explorer nor VisDB have any mechanism for displaying pictures of objects • In some cases images are very helpful • If we are looking for a house then we will be comparing a relatively small number of houses, each of which is a close neighbour (in terms of attributes) of the others • Instead of arranging the attributes in a linear manner, they are arranged radially with the currently examined house at the centre • Houses appear on all attribute axes • A house is selected by dragging it to the centre, at which point all of the axes are rearranged

  24. Rooms Area Price Travel time Time

  25. Musical Visualisation • Many fields use symbolic notations that are unfamiliar to non-experts • Music • Choreography • Mathematics… • Musical notation makes it relatively simple to read a piece of music but more difficult to compose music • Often music is composed on an instrument then transcribed into musical notation • It is now possible to play an instrument and have a computer automatically convert it into a paper-based notation that other people can read • Even so, there are a number of different musical notations in use today…

  26. MusicNotations AmazingGracein ‘standard’ notation Thesamephraseinnumericalnotation

  27. Tablature for stringedinstruments LachrymaebyJohnDowlandinlutetab StairwaytoHeavenbyLed Zeppelin inguitartab

  28. MusicVisualisations • Readinganymusicalnotation is a skillthathastobelearned – otherwiseyoucanseeifthemusicgoesupordown, butthat’s aboutit • Anotheralternative is toshowasananimation not onlythe ‘upanddown’ of thenotesbutalsothe ‘length’ • Eg, http://www.youtube.com/watch?v=ipzR9bhei_o

  29. Musical Visualisation • To make composition easier for lay people instruments have been designed that give visual feedback as well as aural feedback • These encourage direct manipulation of objects in order to control the sound that is produced • http://www.grotrian.de/spiel/e/spiel_win.html

  30. Choreography Waltzinsquare, man’s steps LaCachucha, byFriedrich Albert Zorn. Beauchamp-Feuillernotation for a Frenchcourante

  31. Choreography

  32. Choreography MarianelaNuñez and Yohei Sasaki in a position from the 'Swan Lake' Act I pas de trois and the Benesh notation for the same position

  33. Summary • Real-world problems are seldom specified exactly and involve examining a local neighbourhood of a domain • This means that command line languages like SQL are often not very useful • Users need to be able to use dynamic queries and get rapid visual feedback • In order to be visualised and explored the data has to be represented in some symbolic method; some fields have very specialised symbolic notations

  34. Coming Soon… • Next lecture: Internal Models • Homework: Read chapter 5 of Information Visualisation (Spence)

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