All times are in EDT (Eastern Daylight Time, UTC/GMT -4 hours).
8:40AM-10:20AM Session 1 (Research)
- Converting Property Graphs to RDF: A Preliminary Study of the Practical Impact of Different Mappings
- Batch Dynamic Algorithm to Find k-Core Hierarchies
- Flexible Application-aware Approximation for Modern Distributed Graph Processing Frameworks
- Efficient Provenance-Aware Querying of Graph Databases with Datalog
- DyGraph: A Dynamic Graph Generator and Benchmark Suite
11:00AM-12:00PM Keynote: Knowledge Graph Representation Learning and Graph Neural Networks for Language Understanding. Jing Huang (Alexa AI, Conversational Understanding) Abstract: As AI technologies become mature in natural language processing, speech recognition and computer vision, “intelligent” user interfaces emerge to handle complex and diverse tasks that require human-like knowledge and reasoning capability. In Part 1, I will present our recent work on knowledge graph representation learning using Graph Neural Networks (GNNs): the first approach is called orthogonal transform embedding (OTE), which integrates graph context into the embedding distance scoring function and improves prediction accuracy on complex relations such as the difficult N-to-1, 1-to-N and N-to-N cases; the second approach is called multi-hop attention GNN (MAGNA), a principled way to incorporate multi-hop context information into every layer of attention computation. MAGNA uses a diffusion prior on attention values, to efficiently account for all paths between the pair of disconnected nodes. Experimental results on knowledge graph completion as well as node classification benchmarks show that MAGNA achieves state-of-the-art results. In Part 2, I will present how we take advantage of GNNs for language understanding and reasoning tasks. We show that combined with large pre-trained language models and knowledge graph embeddings, GNNs are proven effective in multi-hop reading comprehension across documents, improving time sensitivity for question answering over temporal knowledge graphs, and constructing robust syntactic information for aspect-level sentiment analysis. Speaker bio: Dr. Jing Huang currently works at Alexa AI as a senior science manager. Her teams work on advanced conversational AI technology, pushing the boundaries of human-machine interactions with better understanding of context, commonsense reasoning, and personal preferences; and building multi-modal creative AI technology. Prior to Amazon, she had been a senior director of JD Technology, and JD AI Research since 2018. She managed teams working on NLP, speech recognition and computer vision. Her teams had achieved top rankings on the NIST SRE (Speaker Recognition Evaluation) in 2018, WikiHop leaderboard in 2019, and HotpotQA leaderboard in 2021. From 2019 to 2021, Dr. Huang also spent part-time at Stanford AI Lab as a visiting scholar, collaborating research with Stanford SNAP and NLP groups on knowledge graph, NLP and dialog system. Before joining JD, Dr. Huang worked as a principal research scientist at Visa Research, and a senior researcher at Apple Siri team. She joined IBM T. J. Watson Research center after she obtained PhD in computer science at Cornell University. At IBM she had worked on automatic speech recognition, natural language processing and audio-visual integration. Dr. Huang graduated from Tsinghua University, Beijing China with BS degree in applied mathematics.
12:00PM-12:30PM Session 2 (Industry)
- Developer experience with Graph in SAP HANA
1:30PM-2:30PM Keynote: Knowledge Graph Semantics. James Hendler (Rensselaer Polytechnic Institute) Abstract: Oh dear, there's that word again - “semantics!” Isn't that what doomed that Semantic Web thing and led to knowledge graphs instead? In fact, many of the same problems, and particularly problems with interoperability, arise again for KGs, and thus we must explore the old problem in this new area. This is even more important when we start to explore the “personal knowledge graph (PKG),” that is, the ability to have private and public information combined in KG technology. In this talk, I discuss how knowledge graphs, PJGs, linked data and, yes, semantics are all critically linked and why the latter is still relevant to the growth and scaling of knowledge graphs into the future - and specifically to the ability to extract better data from them. Speaker bio: James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI. He also is acting director of the RPI-IBM Artificial Intelligence Research Collaboration and serves as a member of the Board of the UK's charitable Web Science Trust. Hendler is a data scientist with specific interests in open government and scientific data, AI and machine learning, semantic data integration and the use of data in government. One of the originators of the Semantic Web, he has authored over 450 books, technical papers, and articles in the areas of Open Data, the Semantic Web, AI, and data policy and governance. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002. In 2004, he became the first computer scientist to serve on the Board of Reviewing editors for Science. In 2010, Hendler was selected as an “Internet Web Expert” by the US government and helped in the development and launch of the US data.gov open data website. In 2013, he was appointed as the Open Data Advisor to New York State and in 2015 appointed a member of the US Homeland Security Science and Technology Advisory Committee. In 2016, became a member of the National Academies Board on Research Data and Information, in 2017 a member of the Director's Advisory Committee of the National Security Directorate of PNNL and in 2021 became chair of the ACM's global Technology Policy Council. Hendler is a Fellow of the AAAI, AAAS, ACM, BCS, IEEE and the US National Academy of Public Administration.
2:30PM-3:30PM Session 3 (Research)
- Anti-Vertex for Neighborhood Constraints in Subgraph Queries
- DynaGraph: Dynamic Graph Neural Networks at Scale
- Multilayer graphs: A unified data model for graph databases
4:30PM-5:15PM Session 4 (Industry)
- LIquid: A relational Graph Database
- Building the Future of Declarative and Relational Knowledge Graphs
- Applications of Dataflow graphs in graph query answering
Posters and Demos
Call for Papers
The goal of GRADES-NDA is to bring together researchers from academia, industry, and government, (1) 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 (2) addressing domain specific challenges or (3) handling noise in real-world graphs.
The workshop will be of interest to researchers in the development of novel data-management applications and systems for large-scale graph analytics. More specifically, the intended audience are, but not limited to, academic and industrial computer scientists interested in databases and data mining, machine learning, data streaming, graph theory and algorithms. Along with novel research work, we encourage submissions with demonstrations and case studies from real-life experiences in various domains such as Social Networks, Biological Network Data, Marketing and Media, Business Data Analysis, Healthcare Data, Cybersecurity etc.
Topics of interest include but are not limited to the following.
- Graph query languages, visualization techniques and querying interfaces, and their effective realization
- Graph platform and parallel platforms, e.g., Flink/Gelly, Titan, SPARK/GraphX, GraphLab/PowerGraph, Giraph, GraphChi etc.
- Network data representation, storage, indexing and querying methods.
- Experiences or techniques for graph specific operations such as traversals or inference/reasoning in the context of large data sets and on the systems that implement those operations.
- RDF data management and analytics
- Dynamic Graphs: managing graph updates; graph stream analytics; analyzing evolution and detection of community structures in real-world evolving graphs
- Mining and machine learning on heterogeneous networks -- knowledge graphs etc.
- Graph summarization and sampling
- Game Theory, Social contagion and Information propagation on networks
- Analytics on dirty, noisy, or uncertain graphs
- Spatial and temporal graph analytics
- Analytics on social, biological, retail, marketing, customer care, financial, healthcare, transportation network data sets
- Descriptions of graph data management use cases and query workloads, and experiences with applying data management technologies in such situations
- Vision and systems papers describing potential or real applications and benefits of graph management
Accepted papers will be published by ACM, indexed by DBLP, and would be available in the ACM DL.
- Paper Submission:
March 23March 25, 2022
April 20April 25, 2022
- Camera Ready Submission:
May 3May 8, 2022
- Workshop Date: June 12, 2022
Authors are invited to submit original, unpublished research papers (full and short), demonstrations and case-studies.
Submissions must follow the latest 2-column ACM Master article "sigconf" proceedings LaTeX template with 10pt font size, and should be double-blind.
Use the following latex command to change the default font size:
Details on the anonymity requirements for submitted manuscripts are present at the SIGMOD 2022 Call for Papers page.
- Full papers should be a maximum of 8 pages in length, excluding references and appendix.
- Case studies should be a maximum of 4 pages in length, excluding references and appendix.
- Short papers and demonstration papers should be a maximum of 4 pages in length, excluding references and appendix.
Submissions will be handled through Easychair. To submit click here.
- Petra Selmer, Neo4j
- Juan F. Sequeda, data.world
- Yinglong Xia, Facebook
- Wenjie Zhang, The University of New South Wales
- Hannes Voigt, Neo4j
- Matei Ripeanu, University of British Columbia
- Jan Hidders, Birkbeck College, University of London
- Marco Serafini, University of Massachusetts Amherst
- Gábor Szárnyas, CWI, Amsterdam
- Essam Mansour, Concordia University
- Arijit Khan, Nanyang Technological University
- Evaggelia Pitoura, University of Ioannina
- Panos Kalnis, King Abdullah University of Science and Technology
- George Fletcher, Eindhoven University of Technology
- Vasileios Trigonakis, Oracle Labs
- Marcelo Arenas, Pontificia Universidad Católica de Chile
Thanks to the generous support of our Sponsors, we are offering awards for selected students to attend GRADES-NDA and SIGMOD in person. Each awardee will receive a stipend to partially cover the expense to attend the conference in-person. Awardees are expected to register to SIGMOD in-person full conference and attend the GRADES-NDA Workshop and later the SIGMOD conference. Students will have to make their own arrangements for travel and accommodation. These awards are only for students who can attend in person. If you cannot attend in person, we advise you to check with SIGMOD travel awards committee (https://2022.sigmod.org/grants.shtml).
Eligibility: Applicants need to be a full-time undergraduate or graduate student. You do not need to have an accepted paper to GRADES-NDA (or SIGMOD) to be eligible. We will primarily prioritize students whose advisors cannot provide financial support. We will also prioritize students who have a GRADES-NDA accepted paper, who are not from North America and Europe, as well as female and minority students.
Application Procedure: To apply for a grant, the student must email the necessary materials to GRADES-NDA chairs (please email both Vasiliki and Semih) by April 22nd. We will notify applicants by April 29th. Please submit the following information in a single PDF file with your application:
- Your full name, school, and email address.
- Your advisor's full name and email address.
- Your CV.
- An at most 1 page (in single column format) abstract, summarizing your thesis research, its connection to graph data management or analytics.
- If you think your presence could help diversity in the GRADES-NDA or SIGMOD community (in terms of the gender, geography/origin, ethnicity or in other ways), please add an additional paragraph with an explanation (does not count towards the 1 page limit for your research).
GRADES-NDA is in its fourth edition, and had successful joint meetings collocated with SIGMOD/PODS 2018, 2019, 2020, and 2021 respectively. Specifically, it is the merger of the GRADES and NDA workshops, which were each independently organized and successfully held at previous SIGMOD-PODS meetings, GRADES since 2013 and NDA since 2016. The organizers of GRADES and NDA mutually agreed upon to aim for a joint meeting from 2018 onwards.