Analytic combinatorics aims to enable precise quantitative predictions of the properties of
large combinatorial structures. The theory has emerged over recent decades as essential …

Data mining is concerned with analysing large volumes of (often unstructured) data to
automatically discover interesting regularities or relationships which in turn lead to better …

Frequent episode discovery is a popular framework for mining data available as a long
sequence of events. An episode is essentially a short ordered sequence of event types and …

Suppose one wants to detect bad or suspicious subsequences in event sequences. Whether
an observed pattern of activity (in the form of a particular subsequence) is significant and …

The problem of characterizing and detecting recurrent sequence patterns such as substrings
or motifs and related associations or rules is variously pursued in order to compress data …

We present a complete analysis of the statistics of number of occurrences of a regular
expression pattern in a random text. This covers “motifs” widely used in computational …

We present a method for a reliable detection of" unusual" sets of episodes in the form of
many pattern sequences, scanned simultaneously for an occurrence as a subsequence in a …

Evaluation of the expected frequency of occurrences of a given set of patterns in a DNA
sequence has numerous applications and has been extensively studied recently. We …

How do you distinguish a cat from a dog by their DNA? Did Shakespeare really write all of
his plays? Pattern matching techniques can offer answers to these questions and to many …

The theory of dependency graphs is a powerful toolbox to prove asymptotic normality of
sums of random variables. In this article, we introduce a more general notion of weighted …