Schedule: Government/Open Data sessions

Location: 116
Robert Kaye (MusicBrainz)
Average rating: ****.
(4.25, 4 ratings)
Too many big data sets live in walled gardens and thus limit innovation to a few players. Creating open data sets levels the playing field and allows open source hackers to participate. Read more.
Location: 212
Francine Bennett (Mastodon C), Duncan Ross (Times Higher Education)
Average rating: ****.
(4.20, 5 ratings)
The data philanthropy movement is growing in Europe. DataKind is actively working to expand it's presence, and DataKind UK is now in it's second year, running successful events and projects. This is the story of the last two events - highlighting how charities have joined the data revolution. Read more.
Location: 212
Lisa Green (Common Crawl), Peter Adolphs (Neofonie)
Average rating: **...
(2.00, 1 rating)
The Web in itself forms a versatile dataset capable of powering most diverse applications. In our joint talk, we will present Common Crawl, an immense collection of Web data made freely available to anyone. We will then introduce MIA and show how this Cloud-based analysis platform and marketplace for data and algorithms enables users to perform analytical tasks on datasets at Web scale. Read more.
Location: 212
Daniele Quercia (Bell Labs)
Average rating: ****.
(4.89, 9 ratings)
How can we change architecture to design more for the people and less for the architects? We present crowd-based solutions with which urban planners can get valuable information about what kind of urban design is attractive to the people. This leads to GPS systems that show you the "most beautiful" path to your destination and to indicators about the beauty of a city. Read more.
Location: 212
Alex Priem (Statistics Netherlands), Edwin De Jonge (Statistics Netherlands)
Average rating: ***..
(3.00, 4 ratings)
Histograms and heatmaps are often used to summarize large data sets. We provide guidelines for using them effectively and efficiently. We illustrate this using the complete Dutch income tax data by looking at distributions in wealth and income. Analysis of this data set is complicated by the large amount of variables. We use clustering techniques to automatically find relevant patterns. Read more.
Location: 212
Bart van Leeuwen (Netage)
Average rating: ****.
(4.00, 1 rating)
It is 2:30 in the night, you are barely awake and racing through the city center of Amsterdam while you hear a 120db horn screaming overhead. You are in a fire truck. Within 4 minutes you will be facing a potential life threatening situation. How do you deal with all the data that can make your work safer in a environment like that? Learn about how we started solving these problems in a agile way. Read more.