Export Citations
2023 Proceeding- Program Chairs:
- Olaf Hartig,
- Yuichi Yoshida
GRADES-NDA 2023 is the sixth joint meeting of the GRADES and NDA workshops, which were each independently organized at previous SIGMOD-PODS meetings, GRADES since 2013 and NDA since 2016. The focus of the GRADES-NDA workshop is the application areas, usage scenarios and open challenges in managing largescale graph-shaped data. The workshop is a forum for exchanging ideas and methods for mining, querying, and learning with real-world network data, developing new common understandings of the problems at hand, sharing of data sets and benchmarks where applicable, and leveraging existing knowledge from different disciplines. GRADES-NDA aims to present technical contributions inside graph, RDF, and other data management systems on massive graphs.
The purpose of this workshop 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 analytics systems, as well as propose and discuss novel methods and techniques towards addressing domain specific challenges and handling noise in real-world graphs.
Proceeding Downloads
The Commercial Side of Graph Analytics: Big Uses, Big Mistakes, Big Opportunities
Connectivity is the cornerstone of our contemporary world, permeating various sectors like retail, communications, biology, and finance. Although this inherent interconnectedness holds substantial meaning and predictive power, harnessing it for practical ...
Graph Feature Management: Impact, Challenges and Opportunities
Graph features are crucial to many applications such as recommender systems and risk management systems. The process to obtain useful graph features involves ingesting data from various upstream data sources, defining the desired graph features for the ...
Better Distributed Graph Query Planning With Scouting Queries
- Tomáš Faltín,
- Vasileios Trigonakis,
- Ayoub Berdai,
- Luigi Fusco,
- Călin Iorgulescu,
- Sungpack Hong,
- Hassan Chafi
Query planning is essential for graph query execution performance. In distributed graph processing, data partitioning and messaging significantly influence performance. However, these aspects are difficult to model analytically, which makes query ...
EAGER: Explainable Question Answering Using Knowledge Graphs
- Andrew Chai,
- Alireza Vezvaei,
- Lukasz Golab,
- Mehdi Kargar,
- Divesh Srivastava,
- Jaroslaw Szlichta,
- Morteza Zihayat
We present EAGER: a tool for answering questions expressed in natural language. Core to EAGER is a modular pipeline for generating a knowledge graph from raw text without human intervention. Notably, EAGER uses the knowledge graph to answer questions and ...
Going with the Flow: Real-Time Max-Flow on Asynchronous Dynamic Graphs
Processing graphs that evolve over time has seen renewed attention. Processing solutions on dynamic graphs (often dubbed "graph streaming" solutions) aim to maintain the state for a graph query as the graph evolves over time, and to timely offer a ...
Future-Time Temporal Path Queries
Most previous research considers processing queries on the current or previous states of a graph. In this paper, we propose processing future-time graph queries, i.e., predicting the output of a query on some future state of the graph. To process future-...
Fast Synthetic Data-Aware Log Generation for Temporal Declarative Models
Business Process Management algorithms are heavily limited by suboptimal algorithmic implementations that cannot leverage state-of-the-art algorithms in the field of relational and graph databases. The recent interest in this discipline for various IT ...
Learning Graph Neural Networks using Exact Compression
Graph Neural Networks (GNNs) are a form of deep learning that enable a wide range of machine learning applications on graph-structured data. The learning of GNNs, however, is known to pose challenges for memory-constrained devices such as GPUs. In this ...
A Demonstration of Interpretability Methods for Graph Neural Networks
Graph neural networks (GNNs) are widely used in many downstream applications, such as graphs and nodes classification, entity resolution, link prediction, and question answering. Several interpretability methods for GNNs have been proposed recently. ...
Recommendations
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| GRADES-NDA'20 | 15 | 9 | 60% |
| GRADES-NDA'19 | 20 | 10 | 50% |
| GRADES-NDA '18 | 26 | 10 | 38% |
| Overall | 61 | 29 | 48% |