The future is big graphs

Sherif Sakr; Angela Bonifati; Hannes Voigt; Alexandru Iosup; Khaled Ammar; Renzo Angles; Walid Aref; Marcelo Arenas; MacIej Besta; Peter A. Boncz; Khuzaima Daudjee; Emanuele Della Valle; Stefania Dumbrava; Olaf Hartig; Bernhard Haslhofer; Tim Hegeman; Jan Hidders; Katja Hose; Adriana Iamnitchi; Vasiliki Kalavri; Hugo Kapp; Wim Martens; M. Tamer Özsu; Eric Peukert; Stefan Plantikow; Mohamed Ragab; Matei R. Ripeanu; Semih Salihoglu; Christian Schulz; Petra Selmer; Juan F. Sequeda; Joshua Shinavier; Gábor Szárnyas; Riccardo Tommasini; Antonino Tumeo; Alexandru Uta; Ana Lucia Varbanescu; Hsiang Yun Wu; Nikolay Yakovets; Da Yan; Eiko Yoneki

doi:10.1145/3434642

The future is big graphs

Sherif Sakr, Angela Bonifati^*, Hannes Voigt, Alexandru Iosup, Khaled Ammar, Renzo Angles, Walid Aref, Marcelo Arenas, MacIej Besta, Peter A. Boncz, Khuzaima Daudjee, Emanuele Della Valle, Stefania Dumbrava, Olaf Hartig, Bernhard Haslhofer, Tim Hegeman, Jan Hidders, Katja Hose, Adriana Iamnitchi, Vasiliki KalavriHugo Kapp, Wim Martens, M. Tamer Özsu, Eric Peukert, Stefan Plantikow, Mohamed Ragab, Matei R. Ripeanu, Semih Salihoglu, Christian Schulz, Petra Selmer, Juan F. Sequeda, Joshua Shinavier, Gábor Szárnyas, Riccardo Tommasini, Antonino Tumeo, Alexandru Uta, Ana Lucia Varbanescu, Hsiang Yun Wu, Nikolay Yakovets, Da Yan, Eiko Yoneki

^*Corresponding author for this work

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Abstract

Graphs are ubiquitous abstractions enabling reusable computing tools for graph processing with applications in every domain. Diverse workloads, standard models and languages, algebraic frameworks, and suitable and reproducible performance metrics will be at the core of graph processing ecosystems in the future. Academics, start-ups, and big tech companies such as Google, Face book, and Microsoft have introduced various systems for managing and processing the growing presence of big graphs. An increasing number of use cases revealed RDBMS performance problems in managing highly connected data, motivating various startups and innovative products, such as Neo4j, Sparksee, and the current Amazon Neptune. Microsoft Trinity along with Azure SQL DB have provided an early distributed database-oriented approach to big graph management.

Original language	English
Journal	Communications of the ACM
Volume	64
Issue number	9
Pages (from-to)	62-71
Number of pages	10
ISSN	0001-0782
DOIs	https://doi.org/10.1145/3434642
Publication status	Published - Sept 2021

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title = "The future is big graphs",

abstract = "Graphs are ubiquitous abstractions enabling reusable computing tools for graph processing with applications in every domain. Diverse workloads, standard models and languages, algebraic frameworks, and suitable and reproducible performance metrics will be at the core of graph processing ecosystems in the future. Academics, start-ups, and big tech companies such as Google, Face book, and Microsoft have introduced various systems for managing and processing the growing presence of big graphs. An increasing number of use cases revealed RDBMS performance problems in managing highly connected data, motivating various startups and innovative products, such as Neo4j, Sparksee, and the current Amazon Neptune. Microsoft Trinity along with Azure SQL DB have provided an early distributed database-oriented approach to big graph management.",

author = "Sherif Sakr and Angela Bonifati and Hannes Voigt and Alexandru Iosup and Khaled Ammar and Renzo Angles and Walid Aref and Marcelo Arenas and MacIej Besta and Boncz, {Peter A.} and Khuzaima Daudjee and Valle, {Emanuele Della} and Stefania Dumbrava and Olaf Hartig and Bernhard Haslhofer and Tim Hegeman and Jan Hidders and Katja Hose and Adriana Iamnitchi and Vasiliki Kalavri and Hugo Kapp and Wim Martens and {\"O}zsu, {M. Tamer} and Eric Peukert and Stefan Plantikow and Mohamed Ragab and Ripeanu, {Matei R.} and Semih Salihoglu and Christian Schulz and Petra Selmer and Sequeda, {Juan F.} and Joshua Shinavier and G{\'a}bor Sz{\'a}rnyas and Riccardo Tommasini and Antonino Tumeo and Alexandru Uta and Varbanescu, {Ana Lucia} and Wu, {Hsiang Yun} and Nikolay Yakovets and Da Yan and Eiko Yoneki",

year = "2021",

month = sep,

doi = "10.1145/3434642",

language = "English",

volume = "64",

pages = "62--71",

journal = "Communications of the ACM",

issn = "0001-0782",

publisher = "Association for Computing Machinery",

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Sakr, S, Bonifati, A, Voigt, H, Iosup, A, Ammar, K, Angles, R, Aref, W, Arenas, M, Besta, M, Boncz, PA, Daudjee, K, Valle, ED, Dumbrava, S, Hartig, O, Haslhofer, B, Hegeman, T, Hidders, J, Hose, K, Iamnitchi, A, Kalavri, V, Kapp, H, Martens, W, Özsu, MT, Peukert, E, Plantikow, S, Ragab, M, Ripeanu, MR, Salihoglu, S, Schulz, C, Selmer, P, Sequeda, JF, Shinavier, J, Szárnyas, G, Tommasini, R, Tumeo, A, Uta, A, Varbanescu, AL, Wu, HY, Yakovets, N, Yan, D & Yoneki, E 2021, 'The future is big graphs', Communications of the ACM, vol. 64, no. 9, pp. 62-71. https://doi.org/10.1145/3434642

TY - JOUR

T1 - The future is big graphs

AU - Sakr, Sherif

AU - Bonifati, Angela

AU - Voigt, Hannes

AU - Iosup, Alexandru

AU - Ammar, Khaled

AU - Angles, Renzo

AU - Aref, Walid

AU - Arenas, Marcelo

AU - Besta, MacIej

AU - Boncz, Peter A.

AU - Daudjee, Khuzaima

AU - Valle, Emanuele Della

AU - Dumbrava, Stefania

AU - Hartig, Olaf

AU - Haslhofer, Bernhard

AU - Hegeman, Tim

AU - Hidders, Jan

AU - Hose, Katja

AU - Iamnitchi, Adriana

AU - Kalavri, Vasiliki

AU - Kapp, Hugo

AU - Martens, Wim

AU - Özsu, M. Tamer

AU - Peukert, Eric

AU - Plantikow, Stefan

AU - Ragab, Mohamed

AU - Ripeanu, Matei R.

AU - Salihoglu, Semih

AU - Schulz, Christian

AU - Selmer, Petra

AU - Sequeda, Juan F.

AU - Shinavier, Joshua

AU - Szárnyas, Gábor

AU - Tommasini, Riccardo

AU - Tumeo, Antonino

AU - Uta, Alexandru

AU - Varbanescu, Ana Lucia

AU - Wu, Hsiang Yun

AU - Yakovets, Nikolay

AU - Yan, Da

AU - Yoneki, Eiko

PY - 2021/9

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AB - Graphs are ubiquitous abstractions enabling reusable computing tools for graph processing with applications in every domain. Diverse workloads, standard models and languages, algebraic frameworks, and suitable and reproducible performance metrics will be at the core of graph processing ecosystems in the future. Academics, start-ups, and big tech companies such as Google, Face book, and Microsoft have introduced various systems for managing and processing the growing presence of big graphs. An increasing number of use cases revealed RDBMS performance problems in managing highly connected data, motivating various startups and innovative products, such as Neo4j, Sparksee, and the current Amazon Neptune. Microsoft Trinity along with Azure SQL DB have provided an early distributed database-oriented approach to big graph management.

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U2 - 10.1145/3434642

DO - 10.1145/3434642

M3 - Journal article

AN - SCOPUS:85113871124

SN - 0001-0782

VL - 64

SP - 62

EP - 71

JO - Communications of the ACM

JF - Communications of the ACM

IS - 9

ER -

The future is big graphs

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Poul Due Jensen Professorate in Big Data and Artificial Intelligence

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The future is big graphs

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Poul Due Jensen Professorate in Big Data and Artificial Intelligence

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