Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Distributed Subgraph Query Processing Using Filtering Scores on Spark

Version 1 : Received: 19 July 2023 / Approved: 20 July 2023 / Online: 20 July 2023 (08:17:56 CEST)

A peer-reviewed article of this Preprint also exists.

Bok, K.; Kim, M.; Lee, H.; Choi, D.; Lim, J.; Yoo, J. Distributed Subgraph Query Processing Using Filtering Scores on Spark. Electronics 2023, 12, 3645. Bok, K.; Kim, M.; Lee, H.; Choi, D.; Lim, J.; Yoo, J. Distributed Subgraph Query Processing Using Filtering Scores on Spark. Electronics 2023, 12, 3645.

Abstract

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.

Keywords

subgraph query; search order; distributed graph; filtering score

Subject

Computer Science and Mathematics, Computer Science

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