  Graph databases (GDB) have recently been arisen to overcome the limits of
traditional databases for storing and managing data with graph-like structure.
Today, they represent a requirement for many applications that manage
graph-like data, like social networks. Most of the techniques, applied to
optimize queries in graph databases, have been used in traditional databases,
distribution systems... or they are inspired from graph theory. However, their
reuse in graph databases should take care of the main characteristics of graph
databases, such as dynamic structure, highly interconnected data, and ability
to efficiently access data relationships. In this paper, we survey the query
optimization techniques in graph databases. In particular, we focus on the
features they have introduced to improve querying graph-like data.
