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Boosting the Impact of Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe Graph-Massivizer: A Horizon Europe Project

Published: 15 April 2023 Publication History

Abstract

We explore the potential of the Graph-Massivizer project funded by the Horizon Europe research and innovation program of the European Union to boost the impact of extreme and sustainable graph processing for mitigating existing urgent societal challenges. Current graph processing platforms do not support diverse workloads, models, languages, and algebraic frameworks. Existing specialized platforms are difficult to use by non-experts and suffer from limited portability and interoperability, leading to redundant efforts and inefficient resource and energy consumption due to vendor and even platform lock-in. While synthetic data emerged as an invaluable resource overshadowing actual data for developing robust artificial intelligence analytics, graph generation remains a challenge due to extreme dimensionality and complexity. On the European scale, this practice is unsustainable and, thus, threatens the possibility of creating a climate-neutral and sustainable economy based on graph data. Making graph processing sustainable is essential but needs credible evidence. The grand vision of the Graph-Massivizer project is a technological solution, coupled with field experiments and experience-sharing, for a high-performance and sustainable graph processing of extreme data with a proper response for any need and organizational size by 2030.

References

[1]
Radu Prodan, Dragi Kimovski, Andrea Bartolini, Michael Cochez, Alexandru Iosup, Evgeny Kharmalov, Joe Roanec, Laurentiu Vasiliu, Ana Lucia Varbanescu. Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe. 2022 IEEE Cloud Summit, 2021, pp. 23--30.
[2]
Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou. AliGraph: a Comprehensive Graph Neural Network Platform. Proceedings of the VLDB Endowment, 12(12):2094--2105, 2019.
[3]
Peter Haase, Daniel Herzig, Artem Kozlov, Andriy Nikolov, Johannes Trame, metaphactory: A platform for knowledge graph management. Semantic Web, 10(6):1109--1125, 2019.
[4]
Fabian Mastenbroek, Georgios Andreadis, Soufiane Jounaid, Wenchen Lai, Jacob Burley, Jaro Bosch, Erwin van Eyk, Laurens Versluis, Vincent van Beek, Alexandru Iosup. OpenDC 2.0: Convenient Modeling and Simulation of Emerging Technologies in Cloud Datacenters. 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 226--236.

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  • (2024)Leveraging Artificial Intelligence for a Sustainable and Climate-Neutral Economy in AsiaStrengthening Sustainable Digitalization of Asian Economy and Society10.4018/979-8-3693-1942-0.ch001(1-21)Online publication date: 17-May-2024

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  1. Boosting the Impact of Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe Graph-Massivizer: A Horizon Europe Project

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    cover image ACM Conferences
    ICPE '23 Companion: Companion of the 2023 ACM/SPEC International Conference on Performance Engineering
    April 2023
    421 pages
    ISBN:9798400700729
    DOI:10.1145/3578245
    This work is licensed under a Creative Commons Attribution International 4.0 License.

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    New York, NY, United States

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    Published: 15 April 2023

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    Author Tags

    1. extreme data
    2. graph processing
    3. serverless computing
    4. sustainability

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    • (2024)Leveraging Artificial Intelligence for a Sustainable and Climate-Neutral Economy in AsiaStrengthening Sustainable Digitalization of Asian Economy and Society10.4018/979-8-3693-1942-0.ch001(1-21)Online publication date: 17-May-2024

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