This paper takes the shortest path discovery to
study efficient relational approaches to graph search queries. We first
abstract three enhanced relational operators, based on which we introduce an
FEM framework to bridge the gap between relational operations and graph
operations. We show new features introduced by recent SQL standards, such as
window function and merge statement, can improve the performance of the FEM
framework. Second, we propose an edge weight aware graph partitioning schema
and design a bi-directional restrictive BFS (breadth-first-search)over
partitioned tables, which improves the scalability and performance without extra
indexing overheads. The final extensive experimental results illustrate our
relational approach with optimization strategies can achieve high scalability
and performance
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