Kamel is back from Madeira (Portugual) where he presented our paper Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation. Madeira is too far from Montreal when you are old and decrepit like me. But he took some great pictures of the place.

I was there last year and it was great:

I have had no end of trouble connecting by ssh to my main Mac Pro. Whenever I would type “ls -1″ in a directory containing many files, the connection would time out. This problem came and went away periodically. Owen pointed me to a sane explanation which has to do with evil firewalls. It looks like I solved my problems (for now) by typing “sudo ifconfig en0 mtu 576″ in a server shell. It has nothing to do with ssh or Apple or MacOS.

I am deeply dissatisfied with Google Mail spam filter. I get 4 or 5 false positives per week, at least 2 of them are critical. It might be the best spam filter in the world, but it does not listen to me. It keeps on marking off as spam perfectly legitimate emails, written in French, from uqam.ca. I have no way to “talk some sense into it.” It is totally asocial.

As Peter points out nobody really knows what science is. Generally speaking, however, I like to distinguish two forms of science.

• Predictive science aims to predict future events based on past observations. It relies on induction. Machine Learning is the embodiment of predictive science.
• Descriptive science aims to describe concisely the universe. Astronomy and biology are descriptive sciences.

The difference between the two is probably a matter of philosophical debate. For example, I can say “the Earth is round” (a description) or “sailing across the sea, you will eventually come back to your starting point” (a prediction). However, the intent is quite different. Gardening and having kids has taught me that the real-world is treacherous. I find it very interesting to describe my kids or my plants, but I am usually quite pessimistic when making predictions about them.

I believe this difference in intent is a fundamental issue in Computer Science. Descriptive people factor in the limitations of their own brain when doing science. They are not after the best system, but rather the best system that they can understand.

Let us play a game. A wizard comes to you and gives you a choice. You can either be handed out the laws of the universe as an algorithm, but in such a form that your brain will be prevented from ever understanding them. Or else, you can be given imperfect laws that you can hope to assimilate within your life time. Which do you pick? If you are a predictive person, you will prefer the perfect laws, at the cost of not understanding them; if you are a descriptive person, you will prefer the laws you can understand, even if they are imperfect.

In gardening—as in research—there are 3 fundamental values one must cultivate.

• Patience. Quick results are possible without much effort. However, it takes a minimum of 3 years for a new garden to reach its maturity. The first year you set the ground, the second year you build-up, and the last year you reap your best results.
• Persistence. You have to continually work at your goals. You do not write great articles or great books the day before the deadline. You must watch over your plants every other day. If you go a week without visiting your garden, many of your fragile plants may die while the sturdy ones may grow out of control.
• Perseverance. You will fail. No matter what. You may have to change your plans drastically, but you should never give up. So, make sure you are having fun.

The 12th International Conference on Extending Database Technology and the 12th International Conference on Database Theory will be held in Saint-Petersburg in March 2009. Papers are due at the end of the Summer. Get busy!

To anyone who worked in industry, academia feels like it is standing still. For example, many Computer Science programs still teach programming as it was done 10 years ago, if you are lucky. Most programs undergo only cosmetic changes over time.

I have the following explanation:

1. Most people are out of touch. This is true everywhere. I remember when Java first came out. Years after Java had mostly caught up with C++ in speed, people still complained that it was slow. I still hear people say that Java is slow. Keeping up with the latest facts is hard. People prefer to rehash the same, again and again. The human brain prefers to avoid change.
2. It takes a long time to build new academic material. Older professors have strong incentives to teach and research the same topics again and again. A similar phenomenon occurs in all large organisations, but professors have academic freedom.
3. Finally, leaving people behind is not an option in academia. Even in large companies, you can leave some people aside. In academia, even one individual who is left behind can create a lot of trouble for others. This is also true in large companies, but most employees do not have as much freedom as a professor: they cannot resist change as strongly as a professor can.

(We could test my explanation by determining whether there is a correlation between the level of academic freedom and the level of conservatism.)

I find it very interesting that increased individual freedom brings about more conservatism.

We have a nice mix of white and black tulips. They really stand out:

I also have a nice rhododendron. I never water it or care for it in any way, and here is how it rewards me:

I do not claim to be an expert at reviewing academic papers, but I have done my share of work. Here is my recipe:

• Reproducibility, (self-)plagiarism and presentation are easy to evaluate and I usually spend quite a bit of time on these issues. Science should be reproducible. (Panos Ipeirotis seems to agree with me.) Plagiarism can be surprisingly hard to detect, but it is also amazingly frequent, so I usually search for a few word cooccurrences in Google. Presentation is, on average, quite poor. Figures are often ugly. Poor English is frequent.
• The relevance and strength of the paper is something I usually have an opinion about. Alas, it is easy to be wrong about the importance of a paper, so I usually do not have much to say unless I have directly worked on the same problems for a couple of years.
• Correctness is hard to check especially if I am not a domain expert. I usually pick up on secondary details. Are the results credible? Do the authors mention some special cases that should have arisen in their analysis or experiments? I must unfortunately admit that I usually cannot be sure that the papers I have reviewed are correct. At best, I can voice an opinion about their credibility.

If you are running MacOS and use the Safari browser, I suggest you have a look at Shiira. It uses the same underlying engine (WebKit), but provides a superior skin.