A distributed vertex-centric approach for pattern matching in massive graphs

Fard, Arash; Nisar, Muhammad Usman; Ramaswamy, Lakshmish; Miller, John A.; Saltz, Matthew

doi:10.1109/bigdata.2013.6691601

Cited by 37 publications

(39 citation statements)

References 12 publications

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“…Gao et al [41] and Fard et al [42] proposed techniques for approximate subgraph pattern matching over vertex-centric frameworks. However, these solutions are approximate, while SPARQL requires exact subgraph pattern matching.…”

Section: Related Workmentioning

confidence: 99%

Combining Vertex-Centric Graph Processing with SPARQL for Large-Scale RDF Data Analytics

Abdelaziz

Harbi

Salihoğlu

et al. 2017

IEEE Trans. Parallel Distrib. Syst.

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Section: Related Workmentioning

confidence: 99%

Combining Vertex-Centric Graph Processing with SPARQL for Large-Scale RDF Data Analytics

Abdelaziz

Harbi

Salihoğlu

et al. 2017

IEEE Trans. Parallel Distrib. Syst.

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“…These hinder its applicability in emerging applications such as social networks and crime detection. Thus, graph simulation [17] and its extensions [18][19][20][21][22] are adopted for pattern matching. Graph simulation preserves the labels and the child relationship of a graph pattern in its match.…”

Section: Related Workmentioning

confidence: 99%

Incremental Graph Pattern Matching Algorithm for Big Graph Data

Zhang

Gao

2018

Scientific Programming

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Graph pattern matching is widely used in big data applications. However, real-world graphs are usually huge and dynamic. A small change in the data graph or pattern graph could cause serious computing cost. Incremental graph matching algorithms can avoid recomputing on the whole graph and reduce the computing cost when the data graph or the pattern graph is updated. The existing incremental algorithm PGC IncGPM can effectively reduce matching time when no more than half edges of the pattern graph are updated. However, as the number of changed edges increases, the improvement of PGC IncGPM gradually decreases. To solve this problem, an improved algorithm iDeltaP IncGPM is developed in this paper. For multiple insertions (resp., deletions) on pattern graphs, iDeltaP IncGPM determines the nodes' matching state detection sequence and processes them together. Experimental results show that iDeltaP IncGPM has higher efficiency and wider application range than PGC IncGPM.

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“…Most practical query languages will at least include the common six (=, !=, <, <=, >, >=). ,6,8,12,16,19,20,24,27,30}, {2,7,13,15,17,21,23,26,29}, {3,4,5,9,11,14,18,22,25,28}, {3,4,5,9,11,14,18,22,25,28}) 29 Dual Simulation Φ(1,2,3,4) → ( {1,6,8,12,16,19,20,24,27,30}, {2,7,13,15,17,21,23,26,29}, {3,4,5,9,14,18,22,25,28}, {3,4,5,9,14,18,22,25,28}) (Fard, Nisar, Ramaswamy, Miller, & Saltz, 2013). Also, a very recent thread of research investigates the usage of view and caching techniques with respect to pattern queries (Fan, Wang, & Wu, 2014), (Fard, Manda, Ramaswamy, & Miller, 2014).…”

Section: Edge-labeled Graphsmentioning

confidence: 99%

“…Strict simulation (Fard, Nisar, Ramaswamy, Miller, & Saltz, 2013) is based on strong simulation, but applies dual simulation first to reduce the number of balls. Balls are only made from vertices that are in the image of Φ.…”

Section: Graph Simulationmentioning

confidence: 99%

Directions for Big Data Graph Analytics Research

Miller¹,

Ramaswamy²,

Kochut³

et al. 2015

STBD

Self Cite

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In the era of big data, interest in analysis and extraction of information from massive data graphs is increasing rapidly. This paper examines the field of graph analytics from a query processing point of view. Whether it be determination of shortest paths or finding patterns in a data graph matching a query graph, the issue is to find interesting characteristics or information content from graphs. Many of the associated problems can be abstracted to problems on paths or problems on patterns. Unfortunately, seemingly simple problems, such as finding patterns in a data graph matching a query graph are surprisingly difficult (e.g., dual simulation has cubic complexity and subgraph isomorphism is -hard). In addition, the iterative nature of algorithms in this field makes the simple MapReduce style of parallel and distributed processing less effective. Still, the need to provide answers even for very large graphs is driving the research. Progress, trends and directions for future research are presented.

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A distributed vertex-centric approach for pattern matching in massive graphs

Cited by 37 publications

References 12 publications

Combining Vertex-Centric Graph Processing with SPARQL for Large-Scale RDF Data Analytics

Combining Vertex-Centric Graph Processing with SPARQL for Large-Scale RDF Data Analytics

Incremental Graph Pattern Matching Algorithm for Big Graph Data

Directions for Big Data Graph Analytics Research

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