Abstract
A regular path query (RPQ) returns node pairs connected by a path whose edge label sequence satisfies the given regular expression. Given a workload of RPQs, selecting the shared subqueries as materialized views to precompute offline can speed up the online processing. Since the available memory is limited, we define the materialized view selection (MVS) problem for RPQs as minimizing the total workload query cost within a memory budget. To tackle the problem's NP-hardness, we design an efficient MVS algorithm based on heuristics. To prevent redundancies in the selected views, we devise the AND-OR directed acyclic graph with closure (AODC) as the multi-RPQ query plan representation for the workload, which encodes the relations between subqueries. In addition to detecting view redundancy, the AODC also incrementally updates itself during view selection. To support query planning, we design a scalable cost and cardinality estimation scheme for full-fledged RPQs, including Kleene closures. Our method, when applied to the Wikidata Query Logs, shows a 9.73× speedup in the total query processing time compared to ad-hoc processing, using the views it selects.
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