OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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Algorithm CountUniqueOccurrences(ArrayOfIntegers list, String testString)
count = 0;
current = list[0];
for (i = 1; i < list.size(); i++)
{
if (list[i] - current >= testString.length())
{
count++;
current = list[i];
}
}
return count;
9.6 Other Uses
Figure 9.6: Algorithm CountUniqueOccurrences
The algorithm presented in this chapter is not restricted to evaluating profile data.
Because it is efficient, offering performance that is linear in the total length of the
input strings when overlapping occurrences are permitted, it can also be applied to
other research areas that examine large strings. Once such area is bioinformatics
which evaluates strings of millions of characters representing DNA strands.
This algorithm may also have applications to data compression. Determining the
substring that contributes the most to a set of strings is equivalent to identifying the
substring that will shrink the set of strings by the largest number of characters if all
occurrences of the substring are removed. This makes the most contributory substring
an ideal candidate for encoding using a short codeword in a data compression
algorithm. The author intends to explore the possibility of applying this algorithm
to Huffman or arithmetic coding as an area of unrelated future work.
9.7 Conclusion
This chapter has defined and solved the Most Contributory Substring Problem. Two
solutions are presented. The first offers a running time that is O(n log |L|) where n is
the total length of the input strings and |L| is the number of input strings. However,