Just came across an inter­est­ing diploma the­sis by Daniel A. Becker, super­vised by Prof. Johannes Bergerhausen:

RANDOM WALK

WHAT DOES RANDOMNESS LOOK LIKE? RANDOM WALK asks this ques­tion and presents exper­i­ments in math­e­mat­ics and physics, show­ing the mys­te­ri­ous inter­ac­tion of chaos and order in ran­dom­ness. The project RANDOM WALK sim­u­lates ran­dom­ness in visu­al­iza­tions, which are easy to under­stand. In this way, it deliv­ers insight into a phe­nom­e­non, which has so far remained unexplained.

Con­grat­u­la­tions to Chris­t­ian for another yummy Master’s the­sis from FH Pots­dam: The Form of Facts and Fig­ures. He col­lected, orga­nized and com­mented on a vari­ety of infor­ma­tion design and visu­al­iza­tion patterns.

I do hope he will put a pdf online!

I read into Yuri Engelhardt’s dis­ser­ta­tion “The Lan­guage of Graph­ics”, and I think it is a fan­tas­tic piece of work. As the title sug­gests, the the­sis sug­gests a lin­guis­ti­cally moti­vated approach to the analy­sis of graph­ics: based from a syn­tac­ti­cal analy­sis of space, objects and their rela­tions, Engel­hardt clas­si­fies and exem­pli­fies dif­fer­ent types of syn­tac­tic struc­tures in which graph­i­cal objects can be arranged in order to con­vey mean­ing. (more…)

My the­sis was awarded a price for the best Mas­ters The­sis at our uni­ver­sity. Although the com­pe­ti­tion was not that large, I am proud nev­er­the­less. I should be pretty much unstop­pable now :)

Just a short post, but another demo is online.

It is a demon­stra­tion of the “elas­tic list” prin­ci­ple for brows­ing multi-facetted data struc­tures. Click any num­ber of list entries to query the data­base for a com­bi­na­tion of the selected attrib­utes. If you cre­ate an “impos­si­ble” con­fig­u­ra­tion, your selec­tion will be reduced until a match is possible.

The exam­ple data is based on the Noble prize win­ners dataset used in the Fla­menco facet browser.

Elas­tic lists enhance tra­di­tional facet brows­ing approaches by • visu­al­iz­ing rel­a­tive pro­por­tions (weights) of meta­data val­ues by size • visu­al­iz­ing unusu­al­ness of a meta­data weight by bright­ness • and ani­mated fil­ter­ing transitions.

In unfil­tered view, the bright­ness shows a trend mea­sure, indi­cat­ing a ris­ing num­ber of prices of the last years.

In fil­tered views, a brighter back­ground indi­cates a higher weight of the meta­data value com­pared to the over­all distribution.

If, for instance, you click “peace” as in the exam­ple above, you will see that “female” and “Switzer­land” are much brighter, indi­cat­ing that the pro­por­tion of women and Swiss is much higher in this con­text than com­pared to the whole data set. That’s inter­est­ing infor­ma­tion and could also be used to char­ac­ter­ize the result set of a key­word query or any other col­lec­tion in terms of its “char­ac­ter­is­tic” meta­data val­ues. Besides that, it fos­ters under­stand­ing of how meta­data val­ues are cor­re­lated with each other, which is often inter­est­ing infor­ma­tion itself.

You can also switch on lit­tle sparklines to see the tem­po­ral dis­tri­b­u­tion of each meta­data value: