3 research outputs found
Indexing Highly Repetitive String Collections
Two decades ago, a breakthrough in indexing string collections made it
possible to represent them within their compressed space while at the same time
offering indexed search functionalities. As this new technology permeated
through applications like bioinformatics, the string collections experienced a
growth that outperforms Moore's Law and challenges our ability of handling them
even in compressed form. It turns out, fortunately, that many of these rapidly
growing string collections are highly repetitive, so that their information
content is orders of magnitude lower than their plain size. The statistical
compression methods used for classical collections, however, are blind to this
repetitiveness, and therefore a new set of techniques has been developed in
order to properly exploit it. The resulting indexes form a new generation of
data structures able to handle the huge repetitive string collections that we
are facing.
In this survey we cover the algorithmic developments that have led to these
data structures. We describe the distinct compression paradigms that have been
used to exploit repetitiveness, the fundamental algorithmic ideas that form the
base of all the existing indexes, and the various structures that have been
proposed, comparing them both in theoretical and practical aspects. We conclude
with the current challenges in this fascinating field
Efficient tree-structured categorical retrieval
Full version of a paper accepted for presentation at the 31st Annual Symposium on Combinatorial Pattern Matching (CPM 2020)We study a document retrieval problem in the new framework where text documents are organized in a {\em category tree} with a pre-defined number of categories. This situation occurs e.g. with taxomonic trees in biology or subject classification systems for scientific literature. Given a string pattern and a category (level in the category tree), we wish to efficiently retrieve the \emph{categorical units} containing this pattern and belonging to the category. We propose several efficient solutions for this problem. One of them uses bits of space and query time, where is the total length of the documents, the size of the alphabet used in the documents and is the total number of nodes in the category tree. Another solution uses bits of space and query time. We finally propose other solutions which are more space-efficient at the expense of a slight increase in query time
Efficient tree-structured categorical retrieval
Full version of a paper accepted for presentation at the 31st Annual Symposium on Combinatorial Pattern Matching (CPM 2020)We study a document retrieval problem in the new framework where text documents are organized in a {\em category tree} with a pre-defined number of categories. This situation occurs e.g. with taxomonic trees in biology or subject classification systems for scientific literature. Given a string pattern and a category (level in the category tree), we wish to efficiently retrieve the \emph{categorical units} containing this pattern and belonging to the category. We propose several efficient solutions for this problem. One of them uses bits of space and query time, where is the total length of the documents, the size of the alphabet used in the documents and is the total number of nodes in the category tree. Another solution uses bits of space and query time. We finally propose other solutions which are more space-efficient at the expense of a slight increase in query time