Incremental Graph Pattern Matching Algorithm for Big Graph Data

Zhang, Lixia; Gao, Jianliang

doi:10.1155/2018/6749561

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…Fan et al proposed in [22] three algorithms for incrementally evaluating graph matching via graph simulation, bounded simulation and subgraph isomorphism. A recent work addressing the same problem was done by [102] where the authors proposed incremental algorithms for performing dual simulation when no more than half of the data graph edges are updated.…”

Section: Synchronous Subgraph-centricmentioning

confidence: 99%

A Survey on Distributed Graph Pattern Matching in Massive Graphs

et al. 2021

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Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed GPM models have emerged as they yield interesting results in a polynomial time. However, massive graphs generated by mostly social networks require a distributed storing and processing of the data over multiple machines, thus, requiring GPM to be revised by adopting new paradigms of big graphs processing, e.g., Think-Like-A-Vertex and its derivatives. This article discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models.

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Section: Synchronous Subgraph-centricmentioning

confidence: 99%

A Survey on Distributed Graph Pattern Matching in Massive Graphs

et al. 2021

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“…Cloudbased infrastructures [170,171] and platforms for analytics [172] are another field of study. In the case of foundations of scientific programming, the work in [173] reviewing the main foundations of scientific programming techniques and the use of pattern matching techniques in large graphs [174] are examples of improvements and works in the scope of software techniques. Finally, applications in coal mining [175], recommendation engines for car sharing services [176], health risk prediction [177], text classification [178], or information security [179] are domains in which data are continuously being generated representing good candidates to apply scientific programming techniques.…”

Section: Data-intensive Engineering Environments and Scientificmentioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

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The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.

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“…To process and analyze a large amount of graph data, various studies on distributed graph processing [12][13][14][15], graph pattern matching [16][17][18], and incremental graph processing [19][20][21][22] have been conducted. Recently, various studies on in-memory caching have been conducted to improve system performance effectively [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

In-Memory Caching for Enhancing Subgraph Accessibility

et al. 2020

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Graphs have been utilized in various fields because of the development of social media and mobile devices. Various studies have also been conducted on caching techniques to reduce input and output costs when processing a large amount of graph data. In this paper, we propose a two-level caching scheme that considers the past usage pattern of subgraphs and graph connectivity, which are features of graph topology. The proposed caching is divided into a used cache and a prefetched cache to manage previously used subgraphs and subgraphs that will be used in the future. When the memory is full, a strategy that replaces a subgraph inside the memory with a new subgraph is needed. Subgraphs in the used cache are managed by a time-to-live (TTL) value, and subgraphs with a low TTL value are targeted for replacement. Subgraphs in the prefetched cache are managed by the queue structure. Thus, first-in subgraphs are targeted for replacement as a priority. When a cache hit occurs in the prefetched cache, the subgraphs are migrated and managed in the used cache. As a result of the performance evaluation, the proposed scheme takes into account subgraph usage patterns and graph connectivity, thus improving cache hit rates and data access speeds compared to conventional techniques. The proposed scheme can quickly process and analyze large graph queries in a computing environment with small memory. The proposed scheme can be used to speed up in-memory-based processing in applications where relationships between objects are complex, such as the Internet of Things and social networks.

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Incremental Graph Pattern Matching Algorithm for Big Graph Data

Cited by 9 publications

References 26 publications

A Survey on Distributed Graph Pattern Matching in Massive Graphs

A Survey on Distributed Graph Pattern Matching in Massive Graphs

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

In-Memory Caching for Enhancing Subgraph Accessibility

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