DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Himchan | ko |
dc.contributor.author | Xiong, Jinjun | ko |
dc.contributor.author | Kim, Min-Soo | ko |
dc.date.accessioned | 2021-10-27T11:50:45Z | - |
dc.date.available | 2021-10-27T11:50:45Z | - |
dc.date.created | 2021-10-27 | - |
dc.date.created | 2021-10-27 | - |
dc.date.issued | 2021-04 | - |
dc.identifier.citation | 37th IEEE International Conference on Data Engineering, ICDE 2021, pp.1512 - 1523 | - |
dc.identifier.issn | 1084-4627 | - |
dc.identifier.uri | http://hdl.handle.net/10203/288377 | - |
dc.description.abstract | As the number of graph applications increases rapidly in many domains, new graph algorithms (or queries) have become more important than ever before. The current two-step approach to develop and test a graph algorithm is very expensive for trillion-scale graphs required in many industrial applications. In this paper, we propose a concept of graph processing simulation, a single-step approach that generates a graph and processes a graph algorithm simultaneously. It consists of a top-down graph upscaling method called V-Upscaler and a graph processing simulation method following the vertex-centric GAS model called T-GPS. Users can develop a graph algorithm and check its correctness and performance conveniently and cost-efficiently even for trillion-scale graphs. Through extensive experiments, we have demonstrated that our single-step approach of V-Upscaler and T-GPS significantly outperforms the conventional two-step approach, although ours uses only a single machine, while the conventional one uses a cluster of eleven machines. | - |
dc.language | English | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | Trillion-scale Graph Processing Simulation based on Top-Down Graph Upscaling | - |
dc.type | Conference | - |
dc.identifier.wosid | 000687830800126 | - |
dc.identifier.scopusid | 2-s2.0-85112865927 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 1512 | - |
dc.citation.endingpage | 1523 | - |
dc.citation.publicationname | 37th IEEE International Conference on Data Engineering, ICDE 2021 | - |
dc.identifier.conferencecountry | GR | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1109/ICDE51399.2021.00134 | - |
dc.contributor.localauthor | Kim, Min-Soo | - |
dc.contributor.nonIdAuthor | Park, Himchan | - |
dc.contributor.nonIdAuthor | Xiong, Jinjun | - |
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