  Many techniques have been developed for the cardinality estimation problem in
data management systems. In this document, we introduce a framework for
cardinality estimation of query patterns over property graph databases, which
makes it possible to analyze, compare and combine different cardinality
estimation approaches. This framework consists of three phases: obtaining a set
of estimates for some subqueries, extending this set and finally combining the
set into a single cardinality estimate for the query. We show that (parts of)
many of the existing cardinality estimation approaches can be used as
techniques in one of the phases from our framework. The three phases are
loosely coupled, this makes it possible to combine (parts of) current
cardinality estimation approaches. We create a graph version of the Join Order
Benchmark to perform experiments with different combinations of techniques. The
results show that query patterns without property constraints can be accurately
estimated using synopses for small patterns. Accurate estimation of query
patterns with property constraints require new estimation techniques to be
developed that capture correlations between the property constraints and the
topology in graph databases.
