  Graphs are ubiquitous and ever-present data structures that have a wide range
of applications involving social networks, knowledge bases and biological
interactions. The evolution of a graph in such scenarios can yield important
insights about the nature and activities of the underlying network, which can
then be utilized for applications such as news dissemination, network
monitoring, and content curation. Capturing the continuous evolution of a graph
can be achieved by long-standing sub-graph queries. Although, for many
applications this can only be achieved by a set of queries, state-of-the-art
approaches focus on a single query scenario. In this paper, we therefore
introduce the notion of continuous multi-query processing over graph streams
and discuss its application to a number of use cases. To this end, we designed
and developed a novel algorithmic solution for efficient multi-query evaluation
against a stream of graph updates and experimentally demonstrated its
applicability. Our results against two baseline approaches using real-world, as
well as synthetic datasets, confirm a two orders of magnitude improvement of
the proposed solution.
