true pink.

update for the past 2 weeks. 

This week i blogged more consistently… had a day which i almost broke down because of the stress. i think i prayed about 3-4 times in a row and that resulted in the very concentrated spot over there. other than that everyday’s been filled with “doing”. this mindless doing is probably attributed most to jap and portfolio, where i spent close to 30 hours on illustrator straight over the past few days…

Updated for the week (wednesday to wednesday)

i added a few new parameters, like STARBUCKS and GOODFOOD. for the chill moments i spend at starbucks doing work and when a meal seems especially tasty on a particular day.

A poster/ graphic to show the hierachy of events that take place when a fire happens in a certain building/ block of flats. Very detailed explanation from start to end.

An interesting representation, a system developed by someone to mark down the way rhythm cycles go when you enter a plane and suffer jet lag from flying half the world to get to another destination. i don’t exactly understand how this whole thing works, but some nice graphics there.

“These panels are the result of my third year information graphics class. The assignment was to choose any sequence, cycle, or evolution and represent it graphically. The information was to be presented in two separate panels that were related but could also function independently of one another.

On the first panel, I chose to represent the circadian rhythm cycle and how they normally work. On the second panel, I presented how circadian rhythms can get off track due to long-distance travel, aka jet lag”

An informational chart on the world’s largest shipping companies that have well…. sunk…

This is how google works.. as written on a webpage, http://www.wired.com/science/discoveries/magazine/16-07/pb_sorting.

It doesnt really explain it in the graphic alone, but nonetheless a clear representation after reading the text.

“ Collect

MapReduce doesn’t depend on a traditional structured database, where information is categorized as it’s collected. We’ll just gather up the full text of every book Google has scanned.

2. Map
You write a function to map the data: “Count every use of every word in Google Books.” That request is then split among all the computers in your army, and each agent is assigned a hunk of data to work with. Computer A gets War and Peace, for example. That machine knows what words that book contains, but not what’s inside Anna Karenina.

3. Save
Each of the hundreds of PCs doing a map writes the results to its local hard drive, cutting down on data transfer time. The computers that have been assigned “reduce” functions grab the lists from the mappers.

4. Reduce
The Reduce computers correlate the lists of words. Now you know how many times a particular word is used, and in which books.

5. Solve
The result? A data set about your data. In our example, the final list of words is stored separately so it can be quickly referenced or queried: “How often does Tolstoy mention Moscow? Paris?” You don’t have to plow through unrelated data to get the answer.”

A cute chart in the shape of a rainbow (semi circle) to illustrate how much this person eats over the 4 seasons in his country, with fruits and vegetables on the outside to show the vast amount, to meat and others on the inside.

A hierachy/ pyramid of popularity of the level of distractivity of social media. with “any kind of actual work” at the bottom. really funny!