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The Future of Graph Analytics

Authors:

Wenjie ZhangAuthors Info & Claims

SIGMOD/PODS '24: Companion of the 2024 International Conference on Management of Data

Pages 544 - 545

https://doi.org/10.1145/3626246.3658369

Published: 09 June 2024 Publication History

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Abstract

In the last two decades, we have been witnessing high demand for graph-based technologies in industry. On the research side, several recent advances have been made about large-scale graph processing, graph analytical systems and graph databases. The landscape of graph query languages is currently evolving with the definition of new standards, and the need for domain-specific languages to express graph algorithmic and analytical primitives will continue and increase in the next future.

In this SIGMOD panel, we will discuss the impact of the above changes on the future of graph analytics. Is there a demand for more expressive languages and libraries for analyzing relationships in a graph? Are new hybrid OLTP/OLAP architectures required with improved performance and scalability? What are the graph analytical workloads and benchmarks that users expect on real-world graph applications? What will be the impact of graph ML on graph analytical systems? How to adapt these systems to the dynamic changes that are ubiquitous for graph-shaped data? These and other questions will be addressed in the panel.

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Cited By

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Yan HYang ZZhang TWen DLuo QUkey N(2025)Approximating Temporal Katz Centrality with Monte Carlo MethodsWeb and Big Data. APWeb-WAIM 2024 International Workshops10.1007/978-981-96-0055-7_1(3-16)Online publication date: 31-Jan-2025
https://doi.org/10.1007/978-981-96-0055-7_1
Ding YYang ZLi SChen LNing HHao KLiu Y(2024)FGAQ: Accelerating Graph Analytical Queries Using FPGAWeb and Big Data10.1007/978-981-97-7244-5_25(357-361)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7244-5_25

Index Terms

The Future of Graph Analytics
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Online analytical processing engines
    2. Query languages
      1. Query languages for non-relational engines

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Published In

SIGMOD/PODS '24: Companion of the 2024 International Conference on Management of Data

June 2024

694 pages

ISBN:9798400704222

DOI:10.1145/3626246

General Chairs:
Pablo Barcelo
Universidad Catolica, Chile
,
Nayat Sanchez-Pi
INRIA Chile
,
Program Chairs:
Alexandra Meliou
University of Massachusetts Amherst, USA
,
S. Sudarshan
Indian Institute of Technology Bombay

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 09 June 2024

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Agence Nationale de la Recherche

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SIGMOD/PODS '24: International Conference on Management of Data

June 9 - 15, 2024

Santiago AA, Chile

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Yan HYang ZZhang TWen DLuo QUkey N(2025)Approximating Temporal Katz Centrality with Monte Carlo MethodsWeb and Big Data. APWeb-WAIM 2024 International Workshops10.1007/978-981-96-0055-7_1(3-16)Online publication date: 31-Jan-2025
https://doi.org/10.1007/978-981-96-0055-7_1
Ding YYang ZLi SChen LNing HHao KLiu Y(2024)FGAQ: Accelerating Graph Analytical Queries Using FPGAWeb and Big Data10.1007/978-981-97-7244-5_25(357-361)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7244-5_25

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In situ graph querying and analytics with graphgen: extended abstract

Research Directions for Big Data Graph Analytics

Aggregation Support for Modern Graph Analytics in TigerGraph

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