  We study distributed algorithms for string matching problem in presence of
wildcard characters. Given a string T (a text), we look for all occurrences of
another string P (a pattern) as a substring of string T . Each wildcard
character in the pattern matches a specific class of strings based on its type.
String matching is one of the most fundamental problems in computer science,
especially in the fields of bioinformatics and machine learning. Persistent
effort has led to a variety of algorithms for the problem since 1960s.
  With rise of big data and the inevitable demand to solve problems on huge
data sets, there have been many attempts to adapt classic algorithms into the
MPC framework to obtain further efficiency. MPC is a recent framework for
parallel computation of big data, which is designed to capture the
MapReduce-like algorithms. In this paper, we study the string matching problem
using a set of tools translated to MPC model. We consider three types of
wildcards in string matching:
  - '?' wildcard: In this setting, the pattern is allowed to contain special
'?' characters or don't cares that match any character of the text. String
matching with don't cares could be solved by fast convolutions, and we give a
constant round MPC algorithm for which by utilizing FFT in a constant number of
MPC rounds.
  - '+' wildcard: '+' wildcard is a special character that allows for arbitrary
repetitions of a character. When the pattern contains '+' wildcard characters,
our algorithm runs in a constant number of MPC rounds by a reduction from
subset matching problem.
  - '*' wildcard: '*' is a special character that matches with any substring of
the text. When '*' is allowed in the pattern, we solve two special cases of the
problem in logarithmic rounds.
