Efficient Algorithms for Association Finding and Frequent Association Pattern Mining

Cheng, Gong; Liu, Daxin; Qu, Yuzhong

doi:10.1007/978-3-319-46523-4_8

Cited by 11 publications

(24 citation statements)

References 21 publications

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“…It is actually a Steiner tree that spans all the query entities. We follow [11,12] to put an upper bound on the diameter of an allowable SA as a compactness constraint. Before giving the formal definition of SA, we review some graph terminology.…”

Section: Definition 1 (Entity-relation Graph) An Entity-relation Gramentioning

confidence: 99%

“…4 finds Q e , which is a maximum sub-query that e is a certificate of the success of (line 10). If Q e is larger than the current Q max , a substitution will be made to update Q max (lines [11][12][13]. If e is less than D 2 hops away from sqe, search will continue and expand the neighbors of e, i.e., N(e) (lines [14][15][16][17][18][19][20][21].…”

Section: Algorithm Designmentioning

confidence: 99%

“…For two query entities, an SA is usually a path or a path-like subgraph that connects them [1,2,3,4,5,6,7,8,9]. More generally, for a set of two or more query entities, an SA is a compact subgraph that connects all the query entities [10,11,12,13,14,15]. For example, the SA shown on the right-hand side of Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Such solutions adopt various kinds of compactness constraints on allowable SAs, mainly in order to bound the search space and achieve satisfactory performance. They place an upper bound on the length of a path-structured SA [2,4,8], on the number of vertices in an SA [10,7,13,15], or on the radius or diameter of an SA [11,12,24]. For example, in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…• We prove that: the success of a relationship query can be indirectly but more efficiently verified by finding an entity (called a certificate) that satisfies a distance-based condition about the query entities. Following that, we devise a polynomial-time algorithm for query relaxation called CertQR, which is more scalable than an intuitive exponential-time solution adapted from a state-of-the-art algorithm for SA search [11].…”

Section: Introductionmentioning

confidence: 99%

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Relaxing relationship queries on graph data

Cheng

2020

Journal of Web Semantics

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In many domains we have witnessed the need to search a large entity-relation graph for direct and indirect relationships between a set of entities specified in a query. A search result, called a semantic association (SA), is typically a compact (e.g., diameter-constrained) connected subgraph containing all the query entities. For this problem of SA search, efficient algorithms exist but will return empty results if some query entities are distant in the graph. To reduce the occurrence of failing query and provide alternative results, we study the problem of query relaxation in the context of SA search. Simply relaxing the compactness constraint will sacrifice the compactness of an SA, and more importantly, may lead to performance issues and be impracticable. Instead, we focus on removing the smallest number of entities from the original failing query, to form a maximum successful sub-query which minimizes the loss of result quality caused by relaxation. We prove that verifying the success of a sub-query turns into finding an entity (called a certificate) that satisfies a distance-based condition about the query entities. To efficiently find a certificate of the success of a maximum sub-query, we propose a best-first search algorithm that leverages distance-based estimation to effectively prune the search space. We further improve its performance by adding two fine-grained heuristics: one based on degree and the other based on distance. Extensive experiments over popular RDF datasets demonstrate the efficiency of our algorithm, which is more scalable than baselines.

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Section: Definition 1 (Entity-relation Graph) An Entity-relation Gramentioning

confidence: 99%