Graph data mining with Arabesque

Eslam Hussein, Abdurrahman Ghanem, Vinicius Vitor Dos Santos Dias, Carlos H.C. Teixeira, Ghadeer Abuoda, Marco Serafini, Georgos Siganos, Gianmarco Morales, Ashraf Aboulnaga, Mohammed Zaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Graph data mining is defined as searching in an input graph for all subgraphs that satisfy some property that makes them interesting to the user. Examples of graph data mining problems include frequent subgraph mining, counting motifs, and enumerating cliques. These problems differ from other graph processing problems such as PageRank or shortest path in that graph data mining requires searching through an exponential number of subgraphs. Most current parallel graph analytics systems do not provide good support for graph data mining. One notable exception is Arabesque, a system that was built specifically to support graph data mining. Arabesque provides a simple programming model to express graph data mining computations, and a highly scalable and efficient implementation of this model, scaling to billions of subgraphs on hundreds of cores. This demonstration will showcase the Arabesque system, focusing on the end-user experience and showing how Arabesque can be used to simply and efficiently solve practical graph data mining problems that would be difficult with other systems.

Original languageEnglish
Title of host publicationSIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1647-1650
Number of pages4
VolumePart F127746
ISBN (Electronic)9781450341974
DOIs
Publication statusPublished - 9 May 2017
Event2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017 - Chicago, United States
Duration: 14 May 201719 May 2017

Other

Other2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017
CountryUnited States
CityChicago
Period14/5/1719/5/17

Fingerprint

Data mining
Demonstrations
Processing

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Hussein, E., Ghanem, A., Dos Santos Dias, V. V., Teixeira, C. H. C., Abuoda, G., Serafini, M., ... Zaki, M. (2017). Graph data mining with Arabesque. In SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data (Vol. Part F127746, pp. 1647-1650). Association for Computing Machinery. https://doi.org/10.1145/3035918.3058742

Graph data mining with Arabesque. / Hussein, Eslam; Ghanem, Abdurrahman; Dos Santos Dias, Vinicius Vitor; Teixeira, Carlos H.C.; Abuoda, Ghadeer; Serafini, Marco; Siganos, Georgos; Morales, Gianmarco; Aboulnaga, Ashraf; Zaki, Mohammed.

SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data. Vol. Part F127746 Association for Computing Machinery, 2017. p. 1647-1650.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hussein, E, Ghanem, A, Dos Santos Dias, VV, Teixeira, CHC, Abuoda, G, Serafini, M, Siganos, G, Morales, G, Aboulnaga, A & Zaki, M 2017, Graph data mining with Arabesque. in SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data. vol. Part F127746, Association for Computing Machinery, pp. 1647-1650, 2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017, Chicago, United States, 14/5/17. https://doi.org/10.1145/3035918.3058742
Hussein E, Ghanem A, Dos Santos Dias VV, Teixeira CHC, Abuoda G, Serafini M et al. Graph data mining with Arabesque. In SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data. Vol. Part F127746. Association for Computing Machinery. 2017. p. 1647-1650 https://doi.org/10.1145/3035918.3058742
Hussein, Eslam ; Ghanem, Abdurrahman ; Dos Santos Dias, Vinicius Vitor ; Teixeira, Carlos H.C. ; Abuoda, Ghadeer ; Serafini, Marco ; Siganos, Georgos ; Morales, Gianmarco ; Aboulnaga, Ashraf ; Zaki, Mohammed. / Graph data mining with Arabesque. SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data. Vol. Part F127746 Association for Computing Machinery, 2017. pp. 1647-1650
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