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

Research output: Contribution to JournalArticleAcademicpeer-review

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 languageEnglish
Pages (from-to)62-71
Number of pages10
JournalCommunications of the ACM
Volume64
Issue number9
Early online date24 Aug 2021
DOIs
Publication statusPublished - Sept 2021

Bibliographical note

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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