R has gained a lot of traction on the scientific community for data analysis, modeling, and exploratory work. I just run into a post by Michael E. Driscoll in his Data Evolution blog about how R is used in Google and Facebook. Nothing new, but what got my attention was ParallelR. If you have been using R for large problems, I am pretty sure you have been wishing that there was some parallelization capabilities....
One of the main handicaps I keep running over and over with Twitter is that there is no concept of groups. The usual story goes along these lines: You get forced to create accounts that behave as groups, make their updates private, and then ask the members to request to follow, and once they follow, that’s it. Yes, a bit convoluted. Yesterday, I ran into GroupTweet that basically automates this process....
Since summer 2007 I have been twittering. It started as way to have a conversation with a bunch of friends scattered all over. Since we were not discussing any world-changing topic, my updates have been kept private. Lately, I have been receiving requests to follow me. So, instead of polluting my original intent, @xllora is now my public Twitter. Hope this gets things a bit simplified. I still wish that Twitter would one day allow you to have a better access control, but, oh well, it is what it is right now....
I am currently working on the distributed execution of flows as part of the Meandre infrastructure—as a part of the SEASR project. One of the pieces to explore is how to push data between machines. No, I am not going to talk about network protocols and the like here, but how you can pass the data around. If you have ever programmed MPI using C/C++ you remember the tedious efforts that requires passing complex data structures around between processes....
by Llorà, X. IlliGAL technical report 2009001. You can download the pdf here. Abstract: Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases—selectorecombinative genetic algorithms and estimation of distribution algorithms—are presented, analyzed, discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era....