Raspberry Pi and XBMC

Raspberry Pi working as a network playerI am  an engineer and a vocational tech geek, therefore always fascinated by technological progress, specifically by the fact that now we have access to cheap solutions, unthinkable only a decade ago. It is the case of the Raspbery PI, a mini computer slightly bigger than a credit card able run Linux to be used for a variety of applications.

The potentiality to solve real world problems is endless as the computer can be fitted with an optional I/O board to control switches, motors and read data from sensors. Continue reading

Oxford Graduation and various thoughts on critical thinking

I was recently in Oxford to receive my degree in Master of Business Administration, or Magister Administrationis Negotii as they say in Latin,and started thinking on what does it really mean to study there.

During the hectic days and nights struggling on books and notes, I mostly noticed the evident aspects of the academic curriculum: finance, operations, accounting, leadership, and the list goes on.

However, I now realize that the difference in Oxford starts from the  basic expectations of the University.


Mortarboards flying by Martin Griffiths

For example it is reasonably possible to get a “Pass” (50-69), all is required is to  understand the basics of the discipline. The difference in this range is only on how thorough the underlying theory is reported. The next band is a distinction (70-100) and the qualification to this range is given only if the student presents the concepts and frameworks taught during the lectures in a critical manner.

To “distinctively” make it, students must point out the limits of the very theory discussed in class and raise original and outstanding issues. Distinctions are seldom awarded.

In Oxford you are expected to think on your own and innovate. There is of course no premium in reporting back a lesser version of the lectures.

Maybe, after all, the most important lesson was just outside the books!

One note: the degree ceremony is something of complicated, typical of the University. Have a look at the brochure.

Do  Fidem!

Network Map of Knowledge and Art V2.0: Preview

I planned to improve this project since last year and now finally I found some more time to dedicate this hobby. The idea is to overcome the major limitation of my previous work create a more realistic map of influence that considers the nested influence: if A influences B and B influences C, then A should influences C as well. This longer chain was not considered in my first model and has deep implications on how to consider authors influence.

Until some time ago I was stuck with two major roadblocks. The first is size of the data – my Wikipedia query contains over 13000 people – so I had to experiment different solutions and implementations to settle with a matrix representation to simplify the computational aspects and the Excel VBA code. 

Sketch of the network representation and pseudo-algorithm

Sketch of the network representation and pseudo-algorithm

Continue reading

Network map of Knowledge and Art: Datasets

I realized I had posted no dataset, so here you are!

You are most welcome to experiment and improve. Please  post your considerations as comments to this article.

Network map of applications

Network to identify the application communities and help users to find the right application.

Network to identify the application communities and help users to find the right application.

One of the problem I am facing is to define a service catalog in a way which is intuitive for the users to select the software they need, hopefully in a self service mode, that is, a android marketplace experience.

One idea is to segment users according to business needs, this however has been proved not easy; for example the category “engineer” is too generic as we hire steam flow engineers, structural ones, and so on… The other option is to create a network map of applications based on consumption data. Applications represents nodes and the co-installation of two applications for the same computer generates an edge, this logic is sketched below. Continue reading

Wikipedia data extraction and The Simpsons

Simpsons Family Picture

Illustration of The Simpsons family

While starting to scratch the surface of the SPARQL and big data possibilities I found an interesting article on how to dig out of Wikipedia the chalkboard gags (the sentences that Bart writes as punishment in the opening sequence of each episode) on Bob DuCharme’s weblog. So I adapted the original query,  created in a 2007 to download every series and to retrieve the coach gags as well (very important).

Since my knowledge is limited, I randomly updated the code until I was able to get the result I wanted (download the full dataset). The code is not at all optimized as it returns a lot of duplicates rows and have the feeling I got the intended results by chance, still now I have achieved two main targets: a) steps on learning SPARQL, b) life long dream 🙂 Continue reading

Network map of Knowledge and Art: possible improvements

Thanks to everybody for reading me and  the good suggestions on how to improve the model. I will work on this again, if you have ideas, please leave a comment to this post. My plan in the next weeks/months is to:

  • Research more the Wikipedia Ontology and the SPARQL language. I only have a shallow knowledge in this field and the data extraction is tricky…for example to make things more difficult, in certain cases the field used is “influenced”, instead of “influenced” or “influenced by”. Any patient volunteer on this to help writing advanced queries is welcome!
  • Add the time dimension. The information is readily available in the Wikipedia infoboxes. This will allow to get a better view on how ideas a propagated and their persistence
  • Improve the influence concept. At the moment the model contemplate a fairly subjective and simple model. Need to add weights and longer chain of influence (e.g.: Cicero => Rousseau => Kant => … => …). This part will be tricky as we are not talking about hard disciplines (try to tell a write his work is deeply influenced by XYZ actors to see either anger or tears…)