Top-k frequent items in data stream
Alon, Matias, and Szegedy proved that finding the frequency of the most frequent of $n$ items requires $\Omega(n)$ space in the worst case in the streaming model, even if you allow a constant number of passes over the input. Since finding the most frequent item gives you a two-pass algorithm for computing its frequency (find in the first pass, track its frequency in the second), we cannot find the most frequent item in sublinear space in the streaming model. 

Usually the way to deal with such a lower bound is to relax the problem. The algorithms Massimo mentions are for the most popular relaxation: the $k$-Heavy Hitters problem ($k$-HH). The $k$-HH problem asks to output all items whose frequency is at least $\frac{m}{k}$, where $m$ is the total frequency of all items (i.e. the length of the stream, in the simplest streaming model). So in $k$-HH we ´expect to see an item only if it actually is very frequent. This problem requires linear space to solve exactly as well: i.e. an algorithm which outputs all and only the items of frequency $\geq \frac{m}{k}$ requires $\Omega(n)$ space in the worst case. For this reason, the problem is usually relaxed a little further: we expect a $k$-HH algorithm to output all items of frequency at least $\frac{m}{k}$ and no items of frequency less than $\frac{(1-\varepsilon)m}{k}$. All algorithms Massimo lists actually do that.

Cormode and Hadjieleftheriou gave a very nice overview of the known $k$-HH algorithms, with brief descriptions, space complexity, and experimental results for performance.