Data Structures and Algorithms in Java[1].pdf - Fulvio Frisone

So the running time for performing all the accesses in this case is O(n 2 ). Thus, the
move-to-front implementation has faster access times for this scenario. This benefit
comes at a cost, however.
Implementing the Move-to-Front Heuristic in Java
In Code Fragment 6.21, we give an implementation of a favorite list using the
move-to-front heuristic. We implement the move-to-front approach in this case by
defining a new class, FavoriteListMTF, which extends the FavoriteList
class and then overrides the definitions of the moveUp and top methods. The
moveUp method in this case simply removes the accessed element from its
present position in the linked list and then inserts this element back in this list at
the front. The top method, on the other hand, is more complicated.
The Trade-Offs with the Move-to-Front Heuristic
Now that we are no longer maintaining the favorite list as a list of entries ordered
by their value's access counts, when we are asked to find the k most accessed
elements, we need to search for them. In particular, we can implement method
top(k) as follows:
1. We copy the entries of our favorite list into another list, C, and we create
an empty list, T.
2. We scan list C k times. In each scan, we find an entry of C with the largest
access count, remove this entry from C, and insert its value at the end of T.
3. We return list T.
This implementation of method top takes O(kn) time. Thus, when k is a constant,
method top runs in O(n) time. This occurs, for example, when we want to get the
"top ten" list. However, if k is proportional to n, then top runs in O(n2) time. This
occurs, for example, when we want a "top 25%" list.
Still, the move-to-front approach is just a heuristic, or rule of thumb, for there are
access sequences where using the move-to-front approach is slower than simply
keeping the favorite list ordered by access counts. In addition, it trades off the
potential speed of performing accesses that possess locality of reference, for a
slower reporting of the top elements.
6.5.3 Possible Uses of a Favorites List
In Code Fragment 6.22, we use an example application of our favorite list
implementations to solve the problem of maintaining the most popular URLs in a
simulated sequence of Web page accesses. This program accesses a set of URLs in
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