| dc.contributor.author | Castellanos, Malu | |
| dc.contributor.author | Hsu, Meichun | |
| dc.contributor.author | Jindal, Alekh | |
| dc.contributor.author | Madden, Samuel R | |
| dc.date.accessioned | 2017-10-04T15:44:09Z | |
| dc.date.available | 2017-10-04T15:44:09Z | |
| dc.date.issued | 2015-12 | |
| dc.identifier.isbn | 978-1-4799-9926-2 | |
| dc.identifier.uri | http://hdl.handle.net/1721.1/111783 | |
| dc.description.abstract | Graph analytics is becoming increasingly popular, with a number of new applications and systems developed in the past few years. In this paper, we study Vertica relational database as a platform for graph analytics. We show that vertex-centric graph analysis can be translated to SQL queries, typically involving table scans and joins, and that modern column-oriented databases are very well suited to running such queries. Furthermore, we show how developers can trade memory footprint for significantly reduced I/O costs in Vertica. We present an experimental evaluation of the Vertica relational database system on a variety of graph analytics, including iterative analysis, a combination of graph and relational analyses, and more complex 1-hop neighborhood graph analytics, showing that it is competitive to two popular vertex-centric graph analytics systems, namely Giraph and GraphLab. | en_US |
| dc.language.iso | en_US | |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.relation.isversionof | http://dx.doi.org/10.1109/BigData.2015.7363873 | en_US |
| dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
| dc.source | arXiv | en_US |
| dc.title | Graph analytics using vertica relational database | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Jindal, Alekh et al. “Graph Analytics Using Vertica Relational Database.” 2015 IEEE International Conference on Big Data (Big Data), October 29 - November 1 2015, Santa Clara, California, USA, Institute of Electrical and Electronics Engineers (IEEE), December 2015: 1191-1200 © 2015 Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
| dc.contributor.mitauthor | Jindal, Alekh | |
| dc.contributor.mitauthor | Madden, Samuel R | |
| dc.relation.journal | 2015 IEEE International Conference on Big Data (Big Data) | en_US |
| dc.eprint.version | Original manuscript | en_US |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
| dspace.orderedauthors | Jindal, Alekh; Madden, Samuel; Castellanos, Malu; Hsu, Meichun | en_US |
| dspace.embargo.terms | N | en_US |
| dc.identifier.orcid | https://orcid.org/0000-0002-7470-3265 | |
| mit.license | OPEN_ACCESS_POLICY | en_US |
