  Graph pattern matching involves finding exact or approximate matches for a
query subgraph in a larger graph. It has been studied extensively and has
strong applications in domains such as computer vision, computational biology,
social networks, security and finance. The problem of exact graph pattern
matching is often described in terms of subgraph isomorphism which is
NP-complete. The exponential growth in streaming data from online social
networks, news and video streams and the continual need for situational
awareness motivates a solution for finding patterns in streaming updates. This
is also the prime driver for the real-time analytics market. Development of
incremental algorithms for graph pattern matching on streaming inputs to a
continually evolving graph is a nascent area of research. Some of the
challenges associated with this problem are the same as found in continuous
query (CQ) evaluation on streaming databases. This paper reviews some of the
representative work from the exhaustively researched field of CQ systems and
identifies important semantics, constraints and architectural features that are
also appropriate for HPC systems performing real-time graph analytics. For each
of these features we present a brief discussion of the challenge encountered in
the database realm, the approach to the solution and state their relevance in a
high-performance, streaming graph processing framework.
