Talk Interactive exploration of complex relational data sets in a web

With the increase of large inter-linked open data sets made available on the web, there is a growing interest in tools that allow to quickly and easily store, transform, query, mine and visualize that data.

In this talk, we focus on our use of the [Protovis](http://mbostock.github.com/protovis/),[D3](http://mbostock.github.com/d3/) Javascript library to interactively visualize the content of a relational database. The underlying framework, CubicWeb, is written in Python and relies on the [numpy](http://numpy.scipy.org/) and [scipy](http://www.scipy.org/) libraries for the intensive numerical computations.

[CubicWeb](http://www.cubicweb.org/) is a semantic web framework written in Python that has been succesfully used in large-scale projects, such as [Data.bnf](https://data.bnf.fr) (French National Library’s opendata) or Collections des musées de Haute-Normandie (museums of Haute-Normandie). Using a browser connected to the server via HTTP, the user can enter queries in a high-level query language, similar to SPARQL but called [RQL](http://docs.cubicweb.org/annexes/rql/language.html#rql), that operates over a relational database [PostgreSQL](http://www.postgresql.org/) in our case.

Data will be loaded from [Geonames](http://geonames.org), [DBpedia](http://dbpedia.org/), various RSS feeds and [http://data.bnf.fr](http://data.bnf.fr).

Using [Protovis](http://mbostock.github.com/protovis/), views will include maps, charts, hierarchies, networks, statistics, etc. A important feature is that any tuple (query, processor, view) has a corresponding url, making all results addressable, linkable and shareable.

More technical details can be found in this blog post : "Data Fast-food": quick interactive exploratory processing and visualization of complex datasets with [CubicWeb](http://www.cubicweb.org/blogentry/2154794).