  As with general graph processing systems, partitioning data over a cluster of
machines improves the scalability of graph database management systems.
However, these systems will incur additional network cost during the execution
of a query workload, due to inter-partition traversals. Workload-agnostic
partitioning algorithms typically minimise the likelihood of any edge crossing
partition boundaries. However, these partitioners are sub-optimal with respect
to many workloads, especially queries, which may require more frequent
traversal of specific subsets of inter-partition edges. Furthermore, they
largely unsuited to operating incrementally on dynamic, growing graphs.
  We present a new graph partitioning algorithm, Loom, that operates on a
stream of graph updates and continuously allocates the new vertices and edges
to partitions, taking into account a query workload of graph pattern
expressions along with their relative frequencies.
  First we capture the most common patterns of edge traversals which occur when
executing queries. We then compare sub-graphs, which present themselves
incrementally in the graph update stream, against these common patterns.
Finally we attempt to allocate each match to single partitions, reducing the
number of inter-partition edges within frequently traversed sub-graphs and
improving average query performance.
  Loom is extensively evaluated over several large test graphs with realistic
query workloads and various orderings of the graph updates. We demonstrate
that, given a workload, our prototype produces partitionings of significantly
better quality than existing streaming graph partitioning algorithms Fennel and
LDG.
