Issue No. 04 - April (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.43
Jun Gao , Key Lab. of High Confidence Software Technol., Peking Univ., Beijing, China
Jiashuai Zhou , Key Lab. of High Confidence Software Technol., Peking Univ., Beijing, China
Jeffrey Xu Yu , Dept. of Syst. Eng., Chinese Univ. of Hong Kong, Hong Kong, China
Tengjiao Wang , Key Lab. of High Confidence Software Technol., Peking Univ., Beijing, China
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.
Finite element methods, Aggregates, Optimization, Context, Scalability, Indexes
Jun Gao, Jiashuai Zhou, J. X. Yu and Tengjiao Wang, "Shortest Path Computing in Relational DBMSs," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 4, pp. 997-1011, 2014.