The future is big graphs

A CommunityView on Graph Processing Systems

Journal article (2021)

Authors

Sherif Sakr University of Tartu
Angela Bonifati Université de Lyon
Hannes Voigt neo4j
A. Iosup Vrije Universiteit Amsterdam, Data-Intensive Systems - EEMCS
Khaled Ammar Borialis Ai
Renzo Angles University of Talca
Walid Aref Purdue University
Marcelo Arenas Puc and Imfd
MacIej Besta ETH Zürich
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Research Group
Data-Intensive Systems (EEMCS) (TU Delft)
Copyright: © 2021 Sherif Sakr, Angela Bonifati, Hannes Voigt, A. Iosup, Khaled Ammar, Renzo Angles, Walid Aref, Marcelo Arenas, MacIej Besta, More Authors
DOI
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https://doi.org/10.1145/3434642
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Published Date

2021

Language

English

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Data-Intensive Systems

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.