Making pattern queries bounded in big graphs

Abstract

It is cost-prohibitive to find matches Q(G) of a pattern query Q in a big graph G. We approach this by fetching a small subgraph GQ of G such that Q(GQ) = Q(G). We show that many practical patterns are effectively bounded under access constraints A commonly found in real life, such that GQ can be identified in time determined by Q and A only, independent of the size |G| of G. This holds no matter whether pattern queries are localized (e.g., via subgraph isomorphism) or non-localized (graph simulation). We provide algorithms to decide whether a pattern Q is effectively bounded, and if so, to generate a query plan that computes Q(G) by accessing GQ, in time independent of |G|. When Q is not effectively bounded, we give an algorithm to extend access constraints and make Q bounded in G. Using real-life data, we experimentally verify the effectiveness of the approach, e.g., about 60% of queries are effectively bounded for subgraph isomorphism, and for such queries our approach outperforms the conventional methods by 4 orders of magnitude.

Querying Big Data by Accessing Small Data

“…Experimenting with real-life Web graphs of billions of nodes and edges, we find that 60% of graph pattern queries via subgraph isomorphism are boundedly evaluable under simple access constraints, and that our bounded-evaluation approach outperforms conventional subgraph isomorphism methods by 4 orders of magnitude on average [11]. ✷ These experimental findings verify that the bounded evaluability analysis yields a practical approach to optimizing queries on big data.…”

“…The need for studying bounded evaluability is evident: if Q is boundedly evaluable, then Q(D) can be computed by accessing (identifying and fetching) a small DQ by using the indices in A, in time determined by Q and A, not by the size of D, no matter how big D grows. Experimenting with real-life data, we find that a large number of queries are boundedly evaluable under a small number of simple access constraints, and that such queries can be efficiently answered in big datasets that satisfy the constraints [11,12].…”

“…There has been work tackling this question [11,12,17]. One idea is to capitalize on a set A of access constraints, which are a combination of indices and cardinality constraints commonly found in practice.…”

“…It showed that it is in PTIME to decide whether a CQ Q is effectively bounded under A, i.e., it has a restricted query plan. With real-life data, the approach was experimentally evaluated for CQ in [12] and for graph pattern queries in [11].…”

Bounded Query Rewriting Using Views

“…Bounded evaluation has proven useful [Cao et al 2014;Cao et al 2015;Cao and Fan 2016]. Experimenting with several real-life datasets, it was shown that under a couple of hundreds of access constraints, 77% of randomly generated conjunctive queries (a.k.a.…”