Big Graph Mining: Frameworks and Techniques

Aridhi, Sabeur; Nguifo, Engelbert Mephu

doi:10.1016/j.bdr.2016.07.002

Cited by 50 publications

(28 citation statements)

References 35 publications

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“…We also notice that most of Big Data frameworks provide graph-related libraries (e.g., Graphx [57] with Spark and Flinkgelly [10] with Flink). Moreover, many graph processing systems have been proposed [4]. Such frameworks include Pregel [39], Graphlab [38], Bladyg [3] and Trinity [47].…”

Section: Big Graph Processingmentioning

confidence: 99%

An experimental survey on big data frameworks

Inoubli

Aridhi

Mezni

et al. 2018

Future Generation Computer Systems

Self Cite

111

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Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks with several representative batch and iterative workloads. This survey is concluded with a presentation of best practices related to the use of studied frameworks in several application domains such as machine learning, graph processing and real-world applications.

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Section: Big Graph Processingmentioning

confidence: 99%

An experimental survey on big data frameworks

Inoubli

Aridhi

Mezni

et al. 2018

Future Generation Computer Systems

Self Cite

111

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“…[17] BigDataFSM by Aridhi and Nguifo in 2016 is also Hadoop based comparison which integrates with PARMA [18] a parallel frequent item-set mining method & PEGASUS [19] which is open-source graph-mining library. [20] IncGM+ FSM Algorithm is developed by Ehab Abdelhamid et al in 2017 is a remarkable algorithm as it works effectively with evolving graphs. This takes Set of Graphs as inputs and produces FSG as output.…”

Section: Existing Fsm Algorithmsmentioning

confidence: 99%

A Survey of Frequent Subgraph Mining Algorithms

Dixit¹,

Khare²

2018

IJET

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Graphs are broadly used data structure. Graphs are very useful in representing/analyzing and processing real world data. Evolving graphs are graphs which are frequently changing in nature. There is either increase or decrease in their size i.e. change in number of edges or/and vertices. Mining is the process done for knowledge discovery in graphs. Detecting specific patterns with their number of repetition more than a predefined threshold in graph is known as frequent subgraph mining or FSM. Real Timed data representing graphs are high volumetric or of very large in size, handling such graphs require processing them with special mechanisms and algorithms. Our review paper discovers present FSM techniques and tries to give their comparative study.

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“…Then, we move to GraphX for graph computation in Sect. 6. After that, we show the key features of Spark Streaming in Sect.…”

Section: At Databricks and Sparkmentioning

confidence: 99%

“…[7,33,61,83,89,90,[93][94][95]. For example, the development of Spark's MLlib began from MLbase 6 project, and then, other projects started to contribute (e.g., KeystoneML 7 ). Spark SQL started from Shark project [84], and then, it became an essential library in Apache Spark.…”

Section: Overview Of Apache Sparkmentioning

confidence: 99%

“…This can simplify the design, implementation and application of complex graph algorithms and pipelines. For more information about existing frameworks and techniques for big graph mining, refer to works such as [6]. -Querying Big Graphs: GraphFrames [24] integrates pattern matching and graph algorithms with Spark SQL to simplify the graph analysis pipeline and enable optimizations across graph and relational queries.…”

Section: Research Highlightsmentioning

confidence: 99%

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Big data analytics on Apache Spark

Salloum

Dautov

Chen

et al. 2016

Int J Data Sci Anal

328

121

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Apache Spark has emerged as the de facto framework for big data analytics with its advanced in-memory programming model and upper-level libraries for scalable machine learning, graph analysis, streaming and structured data processing. It is a general-purpose cluster computing framework with language-integrated APIs in Scala, Java, Python and R. As a rapidly evolving open source project, with an increasing number of contributors from both academia and industry, it is difficult for researchers to comprehend the full body of development and research behind Apache Spark, especially those who are beginners in this area. In this paper, we present a technical review on big data analytics using Apache Spark. This review focuses on the key components, abstractions and features of Apache Spark. More specifically, it shows what Apache Spark has for designing and implementing big data algorithms and pipelines for machine learning, graph analysis and stream processing. In addition, we highlight some research and development directions on Apache Spark for big data analytics.

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Big Graph Mining: Frameworks and Techniques

Cited by 50 publications

References 35 publications

An experimental survey on big data frameworks

An experimental survey on big data frameworks

A Survey of Frequent Subgraph Mining Algorithms

Big data analytics on Apache Spark

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