It seems to me that he claims that "'Energy from the sun is free" not that the station is free. Now maybe he just makes it free as he says he wants to inspire people and had some public funding to pay for this piece. Where does the angst against the word "free" come from here? Peace, love and understanding, dude:)
I have a PhD in CS and in one the courses I had to take they actually said exactly this. That a PhD is maybe 5% about good ideas, passion, insights, etc. and rest is about persistence. Just hang in there and keep pushing and you will get it. I have also seen a plenty of PhD's in CS that I have no idea what is the real science or contribution in there, besides having implemented something that the industry was asking for. Mine is not necessarily much different. When you count the number of PhD's, publications, etc. in the academia as your performance score and one of the main basis for funding and salary etc., this is obviously what you get. Same goes for academia these days being in bed with the industry. Even in industrial research I always see them trying to push in because everyone is hunting for funding where-ever they can get it. Of course, once they get the funding its another matter if you hear from them.
It seems that today the PhD is more like what MSc was 20 years ago considering how many are pushed to get it. That is no necessarily a bad thing, education is good and I learned a lot of useful things myself, including different ways of thinking etc. I am still doing research in CS field, but the industry seems more attractive these days. Especially since if you don't play with the old farts and their views or just be in the "inner circle" it is hard to get in. The academia is just like any other field where the core group is hugging each other to keep their positions and power. Funding is always a huge job with all the competition (as mentioned here), and sucking up to everyone in industry and academia, well, sucks:). Too bad where I live the PhD is mostly considered as disconnected from real life and real problems, and it is a bit hard to find some place to put your skills into practice. So it seems a bit hard to figure where to fit yourself:)
Besides all the whining, I think there are plenty of useful skills to be learned for industrial application in a PhD process. But how to make best use of them in general is perhaps something to improve..
are you talking about a trace or a data analysis tool? if you plan to use LTT to get a trace and then help the user analyse it, maybe you are more into analysis than tracing. then your question could be a bit misleading. Anyway, you would probably end up trying it all out, adding some features to make it all easier to trace as you try to use the existing stuff and analyse the results and so on as you progress.
And if you are into trace data analysis (as opposed to tracing) then your domain of kernel trace data analysis is just one application of data analysis. there you need to look into data analysis methods, statistical methods, machine learning, etc. depending on what kind of analysis you like and need. it is somewhat different depending on your goals such as performance data, behaviour trace and analysis, etc.. for some more behaviour related stuff you can look into domains of program comprehension, behaviour analysis and modeling in general, software reverse engineering, specification mining, etc.
anyway, at least i would be interested to see some results on this kind of stuff if you go with it and have some means to follow on it and provide feedback..mainstream or not most of this stuff never ends up anywhere or is available at all.
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It seems to me that he claims that "'Energy from the sun is free" not that the station is free. Now maybe he just makes it free as he says he wants to inspire people and had some public funding to pay for this piece. Where does the angst against the word "free" come from here? Peace, love and understanding, dude :)
I have a PhD in CS and in one the courses I had to take they actually said exactly this. That a PhD is maybe 5% about good ideas, passion, insights, etc. and rest is about persistence. Just hang in there and keep pushing and you will get it. I have also seen a plenty of PhD's in CS that I have no idea what is the real science or contribution in there, besides having implemented something that the industry was asking for. Mine is not necessarily much different. When you count the number of PhD's, publications, etc. in the academia as your performance score and one of the main basis for funding and salary etc., this is obviously what you get. Same goes for academia these days being in bed with the industry. Even in industrial research I always see them trying to push in because everyone is hunting for funding where-ever they can get it. Of course, once they get the funding its another matter if you hear from them. It seems that today the PhD is more like what MSc was 20 years ago considering how many are pushed to get it. That is no necessarily a bad thing, education is good and I learned a lot of useful things myself, including different ways of thinking etc. I am still doing research in CS field, but the industry seems more attractive these days. Especially since if you don't play with the old farts and their views or just be in the "inner circle" it is hard to get in. The academia is just like any other field where the core group is hugging each other to keep their positions and power. Funding is always a huge job with all the competition (as mentioned here), and sucking up to everyone in industry and academia, well, sucks :). Too bad where I live the PhD is mostly considered as disconnected from real life and real problems, and it is a bit hard to find some place to put your skills into practice. So it seems a bit hard to figure where to fit yourself :)
Besides all the whining, I think there are plenty of useful skills to be learned for industrial application in a PhD process. But how to make best use of them in general is perhaps something to improve..
are you talking about a trace or a data analysis tool? if you plan to use LTT to get a trace and then help the user analyse it, maybe you are more into analysis than tracing. then your question could be a bit misleading. Anyway, you would probably end up trying it all out, adding some features to make it all easier to trace as you try to use the existing stuff and analyse the results and so on as you progress. And if you are into trace data analysis (as opposed to tracing) then your domain of kernel trace data analysis is just one application of data analysis. there you need to look into data analysis methods, statistical methods, machine learning, etc. depending on what kind of analysis you like and need. it is somewhat different depending on your goals such as performance data, behaviour trace and analysis, etc.. for some more behaviour related stuff you can look into domains of program comprehension, behaviour analysis and modeling in general, software reverse engineering, specification mining, etc. anyway, at least i would be interested to see some results on this kind of stuff if you go with it and have some means to follow on it and provide feedback..mainstream or not most of this stuff never ends up anywhere or is available at all.