A survey of current challenges in partitioning and processing of graph-structured data in parallel and distributed systems

Adoni, Hamilton Wilfried Yves; Nahhal, Tarik; Krichen, Moez; Aghezzaf, Brahim; Elbyed, Abdeltif

doi:10.1007/s10619-019-07276-9

Cited by 22 publications

(34 citation statements)

References 56 publications

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“…The mathematical model of the k-partition problem with constraints could be encoded using the following system [8]:…”

Section: Formalismmentioning

confidence: 99%

“…Thus, numerous algorithms appeared [3]. In a survey paper, Adoni et al [8] presented two search techniques: "local" or "global". Local search algorithms begin with an arbitrarily chosen preliminary partition to progress towards a global graph partitioning ("vertex-centric" and "edge-centric") [21].…”

Section: Related Workmentioning

confidence: 99%

“…Local search algorithms begin with an arbitrarily chosen preliminary partition to progress towards a global graph partitioning ("vertex-centric" and "edge-centric") [21]. The downside of this strategy is that the initial choice influences the quality of the obtained results [8]. In comparison, the global search approaches are based on the entire graph ("partition-centric" [21]).…”

Section: Related Workmentioning

confidence: 99%

“…Solving these problems can contribute to efficiently manage the new trend technologies such as big data (e.g., dataViz), distributed systems (e.g., Hadoop [14] and Spark [15]) or future communication networks (e.g., 5G or IoT). Network analysis is widely used in various domains where experimenting relies on large-scale dataset structured as graph data and each information is stored in a vertex an the edges modelize interactions between vertices [8].…”

mentioning

confidence: 99%

“…Partitioning means either partitioning edges or vertices [8,21]. In general, a k-partition corresponds to vertices partitioning [8]. Figure 2 illustrates examples of a 3-partition.…”

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confidence: 99%

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DHPV: a distributed algorithm for large-scale graph partitioning

et al. 2020

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Big graphs are part of the movement of “Not Only SQL” databases (also called NoSQL) focusing on the relationships between data, rather than the values themselves. The data is stored in vertices while the edges model the interactions or relationships between these data. They offer flexibility in handling data that is strongly connected to each other. The analysis of a big graph generally involves exploring all of its vertices. Thus, this operation is costly in time and resources because big graphs are generally composed of millions of vertices connected through billions of edges. Consequently, the graph algorithms are expansive compared to the size of the big graph, and are therefore ineffective for data exploration. Thus, partitioning the graph stands out as an efficient and less expensive alternative for exploring a big graph. This technique consists in partitioning the graph into a set of k sub-graphs in order to reduce the complexity of the queries. Nevertheless, it presents many challenges because it is an NP-complete problem. In this article, we present DPHV (Distributed Placement of Hub-Vertices) an efficient parallel and distributed heuristic for large-scale graph partitioning. An application on a real-world graphs demonstrates the feasibility and reliability of our method. The experiments carried on a 10-nodes Spark cluster proved that the proposed methodology achieves significant gain in term of time and outperforms JA-BE-JA, Greedy, DFEP.

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“…The mathematical model of the k-partition problem with constraints could be encoded using the following system [8]:…”

Section: Formalismmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

“…Partitioning means either partitioning edges or vertices [8,21]. In general, a k-partition corresponds to vertices partitioning [8]. Figure 2 illustrates examples of a 3-partition.…”

mentioning

confidence: 99%

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DHPV: a distributed algorithm for large-scale graph partitioning

et al. 2020

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Multi-path Coverage of All Final States for Model-Based Testing Theory Using Spark In-memory Design

Adoni

Krichen

Nahhal

et al. 2020

Lecture Notes in Computer Science

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This paper deals with an efficient and robust distributed framework for finite state machine coverage in the field model based testing theory. All final states coverage in large-scale automaton is inherently computing-intensive and memory exhausting with impractical time complexity because of an explosion of the number of states. Thus, it is important to propose a faster solution that reduces the time complexity by exploiting big data concept based on Spark RDD computation. To cope with this situation, we propose a parallel and distributed approach based on Spark in-memory design which exploits A* algorithm for optimal coverage. The experiments performed on multi-node cluster prove that the proposed framework achieves significant gain of the computation time.

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OLPGP: An Optimized Label Propagation-Based Distributed Graph Partitioning Algorithm

Ren

2022

Communications in Computer and Information Science

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A survey of current challenges in partitioning and processing of graph-structured data in parallel and distributed systems

Cited by 22 publications

References 56 publications

DHPV: a distributed algorithm for large-scale graph partitioning

DHPV: a distributed algorithm for large-scale graph partitioning

Multi-path Coverage of All Final States for Model-Based Testing Theory Using Spark In-memory Design

OLPGP: An Optimized Label Propagation-Based Distributed Graph Partitioning Algorithm

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