Distance-Aware Selective Online Query Processing Over Large Distributed Graphs

Zhang, Xiaofei; Chen, Lei

doi:10.1007/s41019-016-0023-z

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…Relational Schema Approach In the context of the urgent need for Web-scale distributed query systems [29], SHARD [19] is designed and developed using the MapReduce framework to address the scalability limitation issue. In terms of data persistence, the metadata of the system is persisted in the Hadoop Distributed File System [23].…”

Section: Related Workmentioning

confidence: 99%

Efficient Subgraph Matching on Large RDF Graphs Using MapReduce

Wang

Chai

et al. 2019

Data Sci. Eng.

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With the popularity of knowledge graphs growing rapidly, large amounts of RDF graphs have been released, which raises the need for addressing the challenge of distributed subgraph matching queries. In this paper, we propose an efficient distributed method to answer subgraph matching queries on big RDF graphs using MapReduce. In our method, query graphs are decomposed into a set of stars that utilize the semantic and structural information embedded RDF graphs as heuristics. Two optimization techniques are proposed to further improve the efficiency of our algorithms. One algorithm, called RDF property filtering, filters out invalid input data to reduce intermediate results; the other is to improve the query performance by postponing the Cartesian product operations. The extensive experiments on both synthetic and real-world datasets show that our method outperforms the close competitors S2X and SHARD by an order of magnitude on average.

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Section: Related Workmentioning

confidence: 99%

Efficient Subgraph Matching on Large RDF Graphs Using MapReduce

Wang

Chai

et al. 2019

Data Sci. Eng.

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“…The path-based query processing method searches subgraphs without executing a subgraph isomorphism test algorithm [36][37][38]43,44]. Starting from a vertex in the graph that matches the query, all paths are searched for the connected neighbor vertices to check whether they match the vertex of the query.…”

Section: Introductionmentioning

confidence: 99%

Distributed Subgraph Query Processing Using Filtering Scores on Spark

Bok,

Kim,

Lee

et al. 2023

Electronics

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As various services have been generating large-scale graphs to represent multiple relationships between objects, studies have been conducted to obtain subgraphs with particular patterns. In this paper, we propose a distributed query processing method to efficiently search a subgraph for a large graph on Spark. To reduce unnecessary processing costs, the search order is determined by filtering scores using the probability distribution. The partitioned queries are searched in parallel in the distributed graph of each slave node according to the search order, and the local search results obtained from each slave node are combined and returned. The query is partitioned in triplets based on the determined search order. The performance of the proposed method is compared with the performance of existing methods to demonstrate its superiority.

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“…With the development of Internet, especially the widespread use of mobile devices, distributed data processing becomes a booming area in both the data management industry and academia [1][2][3][4][5]. Nowadays, the volume, richness and diversity of data challenges traditional metric similarity query processing in both space and time.…”

Section: Introductionmentioning

confidence: 99%

Distributed Similarity Queries in Metric Spaces

et al. 2019

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Similarity queries, including range queries and k nearest neighbor (kNN) queries, in metric spaces have applications in many areas such as multimedia retrieval, computational biology and location-based services. With the growing volumes of data, a distributed method is required. In this paper, we propose an Asynchronous Metric Distributed System (AMDS), to support efficient metric similarity queries in the distributed environment. AMDS uniformly partitions the data with the pivot-mapping technique to ensure the load balancing, and employs publish/subscribe communication model to asynchronous process large scale of queries. The employment of asynchronous processing model also improves robustness and efficiency of AMDS. In addition, we develop efficient similarity search algorithms using AMDS. Extensive experiments using real and synthetic data demonstrate the performance of metric similarity queries using AMDS. Moreover, the AMDS scales sublinearly with the growing data size.

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Distance-Aware Selective Online Query Processing Over Large Distributed Graphs

Cited by 10 publications

References 33 publications

Efficient Subgraph Matching on Large RDF Graphs Using MapReduce

Efficient Subgraph Matching on Large RDF Graphs Using MapReduce

Distributed Subgraph Query Processing Using Filtering Scores on Spark

Distributed Similarity Queries in Metric Spaces

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