Effective caching techniques for accelerating pattern matching queries

Fard, Arash; Manda, Satya Vikas; Ramaswamy, Lakshmish; Miller, John A.

doi:10.1109/bigdata.2014.7004266

Cited by 6 publications

(2 citation statements)

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“…Tight simulation can be modified to produce results closer to subgraph isomorphism by using cardinality restrictions on child and parent matches to push results towards one-to-one correspondences. This modification is referred to as Cardinality Restricted (CAR)-tight simulation (Fard, Manda, Ramaswamy, & Miller, 2014). For ℎ v ( , s ) to be true, in addition to the constraints for tight simulation, the child count for each label must be at least as large for vertex s ∈ .…”

Section: Graph Simulationmentioning

confidence: 99%

“…,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). Moreover, real-world data graphs are evolving over time; i.e., there are minor changes in their structure through the time.…”

Section: Edge-labeled Graphsmentioning

confidence: 99%

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Directions for Big Data Graph Analytics Research

Miller¹,

Ramaswamy²,

Kochut³

et al. 2015

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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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Section: Graph Simulationmentioning

confidence: 99%