The goal of GRADES-NDA is to bring together researchers from academia, industry, and government to create a forum for discussing recent advances in (large-scale) graph data management and graph analytics systems, as well as propose and discuss novel methods and techniques towards addressing domain-specific challenges in real-world graphs.

Keynote Speakers

We are very honored to host the following two keynote speakers this year.

• Ioana Manolescu  Inria & Ecole Polytechnique
  Ioana Manolescu is a Senior Researcher at Inria, the national french institute of research in computer science and applied mathematics, and a part-time professor at Ecole Polytechnique, France's leading engineering school. Her main research interests are data integration, semistructured data management, and DB+AI methods and tools for data journalism and journalistic fact checking. She has co-authored 170 articles, several books, and is a recipient of the ACM SIGMOD 2020 Contribution Award. She has been a member of the SIGMOD Executive Committee and of the PVLDB Endowment Board, an Editor in chief of the SIGMOD Record, and a general chair of the ICDE conference.
  Keynote title: The Real World is Graph-Structured: Retrieving Meaning from Heterogeneous Data
  Abstract: Integrating heterogeneous data is an old problem with many successive twists. Challenging data integration problems often feature heterogeneity not only at the level of the schema, but also that of the data model. In the ConnectionLens project, we have shown that heterogeneous data of many semistructured models can be converted in fine-granularity graphs, which can be enriched and densified with the help of information extraction. Such fine-grain graphs, however, are hard to grasp for non-technical users. To help them get acquainted with a dataset, we devised an abstraction method, which identifies, in fine-granularity data graphs, structured objects endowed with an internal structure, and relationships between them. Our approach, implemented in a system called Abstra, inputs a semistructured dataset, and outputs a representation thereof resembling an Entity-Relationship dataset. In contrast with traditional E-R models, our entities may feature deep nesting, reflecting the nested and possibly recursive structure present in some data models. Thus, ConnectionLens with Abstra provide an automated way of "rescuing" the conceptual model, which we argue is best viewed as a graph, behind any application dataset.
  (Joint work with Nelly Barret, Prajna Upadhyay and many other colleagues)
• t.b.d.

Accepted Papers

The following papers have been accepted for this year's workshop.

• Understanding High-Performance Subgraph Pattern Matching: A Systems Perspective
  Akshit Sharma, Dinesh Mehta, and Bo Wu
• Space & Time Efficient Leapfrog Triejoin
  Diego Arroyuelo, Daniela Campos, Adrián Gómez Brandón, Gonzalo Navarro, Carlos Rojas, and Domagoj Vrgoc
• TelarKG: a Knowledge Graph of Chile's Constitutional Process
  Renzo Angles, Vicente Calisto, Javiera Díaz, Sebastián Ferrada, Aidan Hogan, Alexander Pinto, Juan Reutter, Carlos Rojas, Henry Rosales-Méndez, Hernan Sarmiento, Etienne Toussaint, and Domagoj Vrgoc
• Scaling Differential Computation for Large-Scale Graph Processing
  Siddhartha Sahu and Semih Salihoglu
• A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model’s Accuracy for Question Answering on Enterprise SQL Databases
  Juan F. Sequeda, Dean Allemang, and Bryon Jacob
• HomeRun: A Cardinality Estimation Advisor for Graph Databases
  Wilco van Leeuwen, George H. L. Fletcher, and Nikolay Yakovets

Program Committee

• Renzo Angles, Universidad de Talca
• Marcelo Arenas, PUC Chile
• Amitabha Bagchi, Indian Institute of Technology, Delhi
• Kaustubh Beedkar, IIT Delhi
• Yang Cao, The University of Edinburgh
• Nathalie Charbel, Neo4j
• Juan Colmenares, Microsoft
• Sourav Dutta, Huawei Research
• George H. L. Fletcher, Eindhoven University of Technology
• Russ Harmer, CNRS & ENS Lyon
• Jan Hidders, Birkbeck College, University of London
• Davide Mottin, Aarhus University
• Nikos Ntarmos, Huawei Technologies R&D (UK) Ltd
• Evaggelia Pitoura, Univ. of Ioannina
• Petra Selmer, Bloomberg
• Marco Serafini, University of Massachusetts Amherst
• Hiroaki Shiokawa, University of Tsukuba
• Vasileios Trigonakis, Oracle Labs
• Hannes Voigt, Neo4j
• Yinghui Wu, Case Western Reserve University
• Yinglong Xia, Facebook
• Yuichi Yoshida, National Institute of Informatics
• Shangdi Yu, MIT

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