Answering pattern match queries in large graph databases via graph embedding

Zou, Lei; Chen, Lei; Özsu, M. Tamer; Zhao, Dongyan

doi:10.1007/s00778-011-0238-6

Cited by 55 publications

(73 citation statements)

References 37 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Much of it (e.g., [11,28,33,12,26,31]) is based on subgraph isomorphism (SI). Lee et al [15] compare five state-of-theart SI algorithms in detail.…”

Section: Related Workmentioning

confidence: 99%

Event pattern matching over graph streams

Song

Chen

et al. 2014

Proc. VLDB Endow.

View full text Add to dashboard Cite

A graph is a fundamental and general data structure underlying all data applications. Many applications today call for the management and query capabilities directly on graphs. Real time graph streams, as seen in road networks, social and communication networks, and web requests, are such applications. Event pattern matching requires the awareness of graph structures, which is different from traditional complex event processing. It also requires a focus on the dynamicity of the graph, time order constraints in patterns, and online query processing, which deviates significantly from previous work on subgraph matching as well. We study the semantics and efficient online algorithms for this important and intriguing problem, and evaluate our approaches with extensive experiments over real world datasets in four different domains.

show abstract

“…Much of it (e.g., [11,28,33,12,26,31]) is based on subgraph isomorphism (SI). Lee et al [15] compare five state-of-theart SI algorithms in detail.…”

Section: Related Workmentioning

confidence: 99%

Event pattern matching over graph streams

Song

Chen

et al. 2014

Proc. VLDB Endow.

View full text Add to dashboard Cite

show abstract

“…Zou et al [12] present a distance-based pattern matching method for queries over a large data graph G. In order to process the large search space, the authors adopt a filter-andrefine framework to answer pattern match queries over the graph. The method is benchmarked against Tedi [11] and Sapper [13].…”

Section: Recent Workmentioning

confidence: 99%

A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint

Nettleton¹,

Dries²

2015

IJCA

View full text Add to dashboard Cite

Approximate sub-graph matching is important in many graph data mining fields. At present, current solutions can be difficult to implement, have an expensive pre-processing phase, or only work for given types of graph. In this paper a novel generic approach is presented which addresses these issues. An approximate sub-graph matcher (A-SGM) calculates the distance between the topological characteristics (footprint) of the sub-graphs to be matched, applying a weighting to the different sub-graph characteristics and those of neighbor nodes. The weights are calibrated for each dataset with a simulated annealing process using sample sets of graph nodes to reduce computational cost, and an exact isomorphism matcher as a fitness function which takes into account how well the match maintains the neighboring node degree distributions. Benchmarking is performed on several state of the art methods and real and synthetic graph datasets to evaluate the precision, recall and computational cost. The results show that the A-SGM is competitive with state of the art methods in terms of precision, recall and execution time.

show abstract

“…RDF semantics states that two RDF graphs are semantically equivalent if they entail one another 2 , and, as underlined by Carrol [2], the important concept for entailment between RDF graphs is subgraph isomorphism, known to be NP-complete. Subgraph isomorphism is at the heart of a recent pattern matching engine for SPARQL by Zou et al [25]. Unfortunately, the authors do not provide an error-tolerant version of their algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…iSPARQL [16] Silk [24] Zou et al [25] Messmer and Bunke [18] PRISSMA RDF-specific between two graphs can be reduced to the computation of graph edit distance: the idea is that differences between graphs can be modelled in terms of operations to apply to graphs, such as adding a node or modifying an arc. Graph edit distance provides the required flexibility for building an error-tolerant subgraph matching algorithm, and supports customized and heterogeneous cost functions (comparing contexts means dealing with data such as location, time, string literals, URIs).…”

Section: Related Workmentioning

confidence: 99%

Error-Tolerant RDF Subgraph Matching for Adaptive Presentation of Linked Data on Mobile

Costabello

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. We present PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system.

show abstract

Answering pattern match queries in large graph databases via graph embedding

Cited by 55 publications

References 37 publications

Event pattern matching over graph streams

Event pattern matching over graph streams

A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint

Error-Tolerant RDF Subgraph Matching for Adaptive Presentation of Linked Data on Mobile

Contact Info

Product

Resources

About