A Partitioning and Index Algorithm for RDF Data of Cloud-Based Robotic Systems

Leng, Yonglin; Chen, Zhikui; Wang, Hongmin; Zhong, Fangming

doi:10.1109/access.2018.2833480

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…In the graph-based scenario of RDF data, this means to provide a graph partitioning strategy to keep the related vertices in identical nodes [23]. In spite of existing RDF graph partitioning approaches [26], no strategy is optimal for all SPARQL queries and RDF structures [3]. Investigating which partition strategy to use in which situation remains an open challenge.…”

Section: Discussionmentioning

confidence: 99%

Towards Making Distributed RDF Processing FLINKer

Azzam

Kirrane

Polleres

2018

2018 4th International Conference on Big Data Innovations and Applications (Innovate-Data)

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In the last decade, the Resource Description Framework (RDF) has become the de-facto standard for publishing semantic data on the Web. This steady adoption has led to a significant increase in the number and volume of available RDF datasets, exceeding the capabilities of traditional RDF stores. This scenario has introduced severe big semantic data challenges when it comes to managing and querying RDF data at Web scale. Despite the existence of various off-the-shelf Big Data platforms, processing RDF in a distributed environment remains a significant challenge. In this position paper, based on an indepth analysis of the state of the art, we propose to manage large RDF datasets in Flink, a well-known scalable distributed Big Data processing framework. Our approach, which we refer to as FLINKer extends the native graph abstraction of Flink, called Gelly, with RDF graph and SPARQL query processing capabilities.

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Section: Discussionmentioning

confidence: 99%

Towards Making Distributed RDF Processing FLINKer

Azzam

Kirrane

Polleres

2018

2018 4th International Conference on Big Data Innovations and Applications (Innovate-Data)

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“…The query author implicitly agrees on the independent relationship among them and writes a query over them. Next, the third prerequisite can be satisfied frequently when applications run in a rich computing environment like Cloud [35]. Moreover, the query author tends to specify a wide interval between evaluations so that the evaluation is finished early before the next evaluation for the safest execution.…”

Section: A Generality Of the Prerequisitesmentioning

confidence: 99%

Proactive Policy for Efficiently Updating Join Views on Continuous Queries Over Data Streams and Linked Data

2019

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Modern data analytic systems benefit from the fusion of streaming data and linked data distributed on the Web. Accessing the linked data at query time is prohibited as usual due to its expensive cost. To reduce the high cost, most of the database systems have used a materialized view (a view) that stores local copies of the data. However, views by conventional maintenance policies such as immediate, deferred, and periodic fail to achieve high accuracy of answers to queries on data streams and linked data. To cope with the limitations, we propose a maintenance policy that releases expensive jobs of copying the latest version of linked data into views at the idle time. In other words, we pre-fetch a portion of linked data in advance according to their update pattern and query evaluation semantics. Our multiple maintenance policies that take into account changes of linked data alleviate the degradation of performance at run-time. Using real-world datasets we report that the proposed method has a significant improvement in terms of the response time, compared to the state-of-the-art methods.

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“…When handling large RDF graphs on a single server, the performance and speed of processing storage make it difficult to service many users [16][17][18][19]. Therefore, a RDF graph partitioning method is required to store and manage data using multiple servers [20][21][22]. The existing RDF partitioning methods focused on minimizing the number of edge-cuts generated by partitioning [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Partitioning Supporting Load Balancing for Distributed RDF Graph Stores

2019

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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...

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A Partitioning and Index Algorithm for RDF Data of Cloud-Based Robotic Systems

Cited by 4 publications

References 29 publications

Towards Making Distributed RDF Processing FLINKer

Towards Making Distributed RDF Processing FLINKer

Proactive Policy for Efficiently Updating Join Views on Continuous Queries Over Data Streams and Linked Data

Dynamic Partitioning Supporting Load Balancing for Distributed RDF Graph Stores

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