Learning to Speed Up Query Planning in Graph Databases
release_2yexjo3eyjfmlorogc3sqawi7a
by
Mohammad Hossain Namaki, F A Rezaur Rahman Chowdhury, Md Rakibul
Islam, Janardhan Rao Doppa, Yinghui Wu
2018
Abstract
Querying graph structured data is a fundamental operation that enables
important applications including knowledge graph search, social network
analysis, and cyber-network security. However, the growing size of real-world
data graphs poses severe challenges for graph databases to meet the
response-time requirements of the applications. Planning the computational
steps of query processing - Query Planning - is central to address these
challenges. In this paper, we study the problem of learning to speedup query
planning in graph databases towards the goal of improving the
computational-efficiency of query processing via training queries.We present a
Learning to Plan (L2P) framework that is applicable to a large class of query
reasoners that follow the Threshold Algorithm (TA) approach. First, we define a
generic search space over candidate query plans, and identify target search
trajectories (query plans) corresponding to the training queries by performing
an expensive search. Subsequently, we learn greedy search control knowledge to
imitate the search behavior of the target query plans. We provide a concrete
instantiation of our L2P framework for STAR, a state-of-the-art graph query
reasoner. Our experiments on benchmark knowledge graphs including DBpedia,
YAGO, and Freebase show that using the query plans generated by the learned
search control knowledge, we can significantly improve the speed of STAR with
negligible loss in accuracy.
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