BRGP: a balanced RDF graph partitioning algorithm for cloud storage

Leng, Yonglin; Chen, Zhikui; Zhong, Fangming; Li, Xiongjiu; Hu, Yueming; Yang, Chao

doi:10.1002/cpe.3896

Cited by 13 publications

(7 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method consisted of 2 main VM migration phases to reduce service prices and execution time, which had been evaluated by experiments. In addition, Leng et al presented a balanced RDF graph partitioning algorithm for storing massive RDF data on cloud. The authors first devise a modularity‐based multilevel label propagation algorithm to partition RDF graph roughly and then use a balanced K‐mediods clustering algorithm for final k‐way partitioning.…”

Section: Themes Of This Special Issuementioning

confidence: 99%

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

Hassan

Yang

Qiu

2017

Concurrency and Computation

View full text Add to dashboard Cite

This special issue is composed of a collection of articles, which provide recent advances in some fields related to Big Data Security and Intelligent Data in Clouds. More precisely, the manuscripts investigate different topics including forensic in cloud-of-things devices, encryption in mobile cloud environments, defense against malicious URL spreading in microblog networks, social network access control model in cloud computing, dynamic resource sharing algorithms, load balancing for cloud computing, cloud-based audio/video streaming techniques, energy-aware scheduling in clouds, sensor network security in cloud computing, as well as real world applications related to industry, manufacturing, and vanets. We hope that the readers could benefit from the articles presented in this special issue and would contribute to these exciting research areas. ACKNOWLEDGMENTSThe guest editors of this special issue wish to express their sincere gratitude to all of the authors who contributed to this issue. We extend our thanks to the reviewers for their hard work and their feedbacks. Likewise, we wish to express our gratitude

show abstract

Section: Themes Of This Special Issuementioning

confidence: 99%

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

Hassan

Yang

Qiu

2017

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Leng et al proposed a technique based on multi‐level modularity optimization to cluster data in the Resource Description Framework (RDF) format, so that the clusters represent a balanced partitioning. The balance with respect to partitioning is achieved by an application of the balanced k‐medoids algorithm and is leveraged to distribute the data across storage nodes in a cloud …”

Section: Related Workmentioning

confidence: 99%

“…The balance with respect to partitioning is achieved by an application of the balanced k-medoids algorithm and is leveraged to distribute the data across storage nodes in a cloud. 10 We now outline some of the recent research attempts in building a scalable spectral clustering technique. Spectral clustering methods require pairwise similarity to be computed for the data points and hence, incur an order of quadratic time to compute the similarity matrix and a cubic time to compute the eigenspace using Singular Value Decomposition.…”

Section: Related Workmentioning

confidence: 99%

“…Several seminal distributed and parallel algorithms have been proposed for clustering by leveraging the modularity metric. () Typical modularity maximization algorithms assign ranks to the vertices or edges and based on the ranks assigned determine the vertices to be moved to the neighboring clusters or the edges to be part of the contraction set. Edges in the contraction set are then coalesced to merge two or more related clusters.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A distributed parallel algorithm for inferring hierarchical groups from large‐scale text corpuses

Seshadri¹,

Mercy²,

Manohar³

2017

Concurrency and Computation

View full text Add to dashboard Cite

Summary We propose a distributed parallel algorithm for inferring the hierarchical groups present in a large‐scale text corpus. The algorithm is designed to deal with corpuses that typically do not fit into the main memory of a workstation computer. The key contribution of this paper lies in its proposal and verification of a parallel distributed algorithm that exploits the advantages of two complementary techniques based on (i) localized modularity optimization and (ii) spectral clustering. Based on our experimental observations, these are complementary in the sense that the former excels at finding coarse groups in a large‐scale network, while the latter demands a heavy memory footprint but is effective in inferring tightly knit fine‐grained groups. Empirical evaluation of the distributed implementation scheme shows that the algorithm exhibits a significant speed‐up when compared to existing algorithms like Louvain and, at the same time, produces better quality clusters than either Louvain or spectral clustering algorithms in terms of the F‐score and Rand index.

show abstract

“…Leng et al proposed a balanced RDF graph partitioning (BRGP) for storing massive RDF data on cloud [42]. BRGP use a modularity-based multi-level label propagation algorithm (MMLP) to partition RDF graph roughly.…”

mentioning

confidence: 99%

Dynamic Partitioning Supporting Load Balancing for Distributed RDF Graph Stores

2019

View full text Add to dashboard Cite

Various resource description framework (RDF) partitioning methods have been studied for the efficient distributed processing of a large RDF graph. The RDF graph has symmetrical characteristics because subject and object can be used interchangeably if predicate is changed. This paper proposes a dynamic partitioning method of RDF graphs to support load balancing in distributed environments where data insertion and change continue to occur. The proposed method generates clusters and subclusters by considering the usage frequency of the RDF graph that are used by queries as the criteria to perform graph partitioning. It creates a cluster by grouping RDF subgraphs with higher usage frequency while creating a subcluster with lower usage frequency. These clusters and subclusters conduct load balancing by using the mean frequency of queries for the distributed server and conduct graph data partitioning by considering the size of the data stored in each distributed server. It also minimizes the number of edge-cuts connected to clusters and subclusters to minimize communication costs between servers. This solves the problem of data concentration to specific servers due to ongoing data changes and additions and allows efficient load balancing among servers. The performance results show that the proposed method significantly outperforms the existing partitioning methods in terms of query performance time in a distributed server. This paper is organized as follows. Section 2 describes the existing graph partitioning methods. Section 3 describes the RDF dynamic partitioning method proposed in this paper. Section 4 describes the results of the performance evaluation to show the excellence of the proposed method. Finally, Section 5 provides the conclusions of this paper and presents future directions. Related WorkHendrickson and Leland proposed a multilevel method for graph partitioning within acceptable execution time [23]. The multilevel partitioning method generates a sequence of smaller graphs approximating the original graph to find easily a good partition via coarsening procedure. In the coarsening step, it merges two vertices connected to an edge and the new vertex retains edges to the union of the neighbors of the merged vertices. The smallest graph is partitioned via a spectral partitioner. The spectral partitioner needs one, two, or three eigenvectors of the Laplacian matrix to partition a graph into two, four, or eight groups, respectively. The partitioning algorithm is able to handle edge and vertex weights, even if the original graph is unweighted. The multilevel partitioning method makes the sums of the vertex weights in each server as equal as possible and the sum of the weights of edges crossing between servers is minimized.Wang and Chiu proposed a RDF graph partitioning method to minimize the number of edges on different servers [40]. This method first scans the RDF graph and stores subgraph with common vertices into the same components. If the connected components of the graph are found, each connected componen...

show abstract

BRGP: a balanced RDF graph partitioning algorithm for cloud storage

Cited by 13 publications

References 33 publications

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

A distributed parallel algorithm for inferring hierarchical groups from large‐scale text corpuses

Dynamic Partitioning Supporting Load Balancing for Distributed RDF Graph Stores

Contact Info

Product

Resources

About