Master data

Title: ICPE'23 GraphSys Workshop Chairs Introduction (Welcome)
Subtitle:
Abstract:

It is our great pleasure to welcome you to the 2023 ACM/SPEC Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems. This is the first such workshop, aiming to facilitate the exchange of ideas and expertise in the broad field of high-performance large-scale graph processing.

Graphs and GraphSys - The use, interoperability, and analytical exploitation of graph data are essential for modern digital economies. Today, thousands of computational methods (algorithms) and findable, accessible, interoperable, and reusable (FAIR) graph datasets exist. However, current computational capabilities lag when faced with the complex workflows involved in graph processing, the extreme scale of existing graph datasets, and the need to consider sustainability metrics in graph-processing operations. Needs are emerging for graph-processing platforms to provide multilingual information processing and reasoning based on the massive graph representation of extreme data in the form of general graphs, knowledge graphs, and property graphs. Because graph workloads and graph datasets are strongly irregular, and involve one or several big data "Vs" (e.g., volume, velocity, variability, vicissitude), the community needs to reconsider traditional approaches in performance analysis and modeling, system architectures and techniques, serverless and "as a service" operation, real-world and simulation-driven experimentation, etc., and provide new tools and instruments to address emerging challenges in graph processing.

Graphs or linked data are crucial to innovation, competition, and prosperity and establish a strategic investment in technical processing and ecosystem enablers. Graphs are universal abstractions that capture, combine, model, analyze, and process knowledge about real and digital worlds into actionable insights through item representation and interconnectedness. For societally relevant problems, graphs are extreme data that require further technological innovations to meet the needs of the European data economy. Digital graphs help pursue the United Nations Sustainable Development Goals (UN SDG) by enabling better value chains, products, and services for more profitable or green investments in the financial sector and deriving trustworthy insight for creating sustainable communities. All science, engineering, industry, economy, and society-at-large domains can leverage graph data for unique analysis and insight, but only if graph processing becomes easy to use, fast, scalable, and sustainable.

GraphSys is a cross-disciplinary meeting venue focusing on state-of-the-art and the emerging (future) graph processing systems. We invite experts and trainees in the field, across academia, industry, governance, and society, to share experience and expertise leading to a shared body of knowledge, to formulate together a vision for the field, and to engage with the topics to foster new approaches, techniques, and solutions.

Keywords:
Publication type: Article in Proceedings (Authorship)
Publication date: 15.04.2023 (Online)
Published by: ICPE '23: Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering, Companion Proceedings
ICPE '23: Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering, Companion Proceedings
to publication
 ( ACM Digital Library; )
Title of the series: -
Volume number: -
First publication: Yes
Version: -
Page: pp. 1 - 2

Versionen

Keine Version vorhanden
Publication date: 15.04.2023
ISBN (e-book): -
eISSN: -
DOI: http://dx.doi.org/10.1145/3578245.3585328
Homepage: https://dl.acm.org/doi/10.1145/3578245.3585328
Open access
  • Available online (not open access)

Assignment

Organisation Address
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020 Klagenfurt am Wörthersee
Austria
   martina.steinbacher@aau.at
http://itec.aau.at/
To organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Categorisation

Subject areas
  • 1020 - Computer Sciences
Research Cluster No research Research Cluster selected
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: II)
Classification raster of the assigned organisational units:
working groups
  • Verteilte Systeme

Cooperations

Organisation Address
Vrije Universiteit Amsterdam
De Boelelaan 1105
1081 HV Amsterdam
Netherlands
https://www.vu.nl/nl/index.aspx
De Boelelaan 1105
NL - 1081 HV  Amsterdam

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