SEA Graph

3rd Workshop on Search, Exploration, and Analysis in Heterogeneous Datastores: Graph Edition

Co-located with ICDE 2024 (, Utrecht, Netherlands)

SEA Graph workshop proposes a unique international venue for researchers and practitioners willing to share their insights, experience, and solutions in the management and analysis of heterogeneous Graph data.

Companies, governments, and organizations are now producing and collecting data from multiple heterogeneous sources, such as transactional data, internet traffic, logs, IoT applications, knowledge bases, and much more. The unprecedented pace in which we produce and consume data calls for methods that organize, retrieve, and analyze such data appropriately. While traditionally data were organized into homogeneous datastores and formats, the current data collection from multiple different sources makes such datastores impractical. Even within the same organization, data dwells in independent silos, each one with a distinct data model and serving a specific application.

In today data management systems, there is a need to expose more flexible and expressive data and query models. Consequently, graphs have attracted considerable attention from the community for their flexibility in modelling, organizing, managing, and querying heterogeneous data. Among these, knowledge graphs have demonstrated high effectiveness as holistic data integration models.

Motivated by the growing relevance of the graph data model (recently added even to the SQL standard) and the recent efforts in the research community, the SEA Graph workshop proposes a unique international venue for researchers and practitioners willing to share their insights, experience, and solutions in the management and analysis of heterogeneous graph data.

The SEA Graph workshop will provide a forum for researchers and practitioners to exchange ideas, results, and visions on challenges in adopting graphs to handle data management, information extraction, exploration, and analysis of heterogeneous data and multiple data models at once.

Workshop Chairs

You can also see the details from the previous edition at EDBT 2020 and previous edition at VLDB 2021.

Topics

SEA Graph aims at gathering researchers and practitioners from various communities related to databases, data management, and knowledge engineering. We gladly accept submissions that present initial ideas and visions, just as much as reports on early results, or reflections on completed projects. The workshop will focus on discussion and interaction, rather than static presentations of what is in the paper. A list of relevant topics is presented below.

The workshop also welcomes papers on negative results

Fundamentals
Algebra and logics for graph databases
Graph query languages, algebra, and logics
Querying and analyzing semantic data lakes and polystores;
Comparison of graph data models to traditional data models
Schemas for graph databases
Semantic web graph data formats (e.g., RDF, OWL)
Graph mining and learning
Mining and profiling of graphs
Information retrieval on graph-structured data
Data explorations on graphs.
Graph pattern matching
Link prediction, clustering, node and graph classification
Automatic learning of graph embeddings or indexes
Extraction of vectors, matrices, and metapaths from graphs (e.g., as input for neural networks)
Graph data quality assessment
Graph data management
Search in graph databases
Flexible query answering on graph-structured data
Intelligent distribution of query processing
Indexing methods for graph processing
Storage systems for large-scale graph databases
Automatic distribution and replication of graph databases
Scalable algorithms for graph management
Applications
Biological and medical graph databases
Social Networks and Citation graphs
Visualizing, browsing, and navigating graph data

We also welcome submissions on thought-provoking applications and emerging uses of graph data management technology in heterogeneous datastores or multi-model databases.

You can also see the details from the previous editions at EDBT 2020 and VLDB 2021.

Program Committee

Workshop Program

Program Schedule
Welcome & Intro

Vision Paper:
The Future of Graph-based Spatial Pattern Matching
Nicole Schneider, Kent O'Sullivan and Hanan Samet

Extended Abstract:
OBDF: OBDA + Data Federation
Zhenzhen Gu, Diego Calvanese, Marco Di Panfilo, Davide Lanti, Alessandro Mosca and Guohui Xiao

Extended Abstract:
View-based Explanations for Graph Neural Networks
Tingyang Chen, Dazhuo Qiu, Yinghui Wu, Arijit Khan, Xiangyu Ke and Yunjun Gao

Break

Keynote:
Graph Data Science for Social Goods: STAR Lab’s Experience
Reynold C.K. Cheng

Vision Paper:
Towards View Management in Graph Databases
Mohanna Shahrad, Yu Ting Gu, Yunjia Zheng and Bettina Kemme

Lunch Break

Vision Paper:
Integrating Complex Pangenome Graphs
Jérôme Arnoux, Angela Bonifati, Alexandra Calteau, Stefania Dumbrava and Guillaume Gautreau

Extended Abstract:
Compact Path Representations for Graph Database Pattern Matching
Wim Martnes, Matthias Niewerth, Tina Popp, Carlos Rojas, Stijn Vansummeren and Domagoj Vrgoc

Panel:
Knowledge Graphs and LLMs: A Relationship under Investigation
moderated by Georgia Koutrika

Break

Vision Paper:
Towards User-Centric Graph Repairs
Amedeo Pachera, Angela Bonifati and Andrea Mauri

Vision Paper:
Graph lenses over any data: the ConnectionLens experience
Oana Balalau, Nelly Barret, Simon Ebel, Théo Galizzi, Ioana Manolescu and Madhulika Mohanty

Experiment & Analysis Paper:
An Empirical Evaluation of Variable-length Record B+Trees on a Modern Graph Database System
Georgios Theodorakis, James Clarkson and Jim Webber

Vision Paper:
Finding the PG schema of any (semi)structured dataset: a tale of graphs and abstraction
Nelly Barret, Tudor Enache, Ioana Manolescu and Madhulika Mohanty

Closing

Keynote:

Graph Data Science for Social Goods: STAR Lab’s Experience
by Prof. Reynold C.K. Cheng

Abstract:

In many metropolitan cities, there is a lack of manpower in social care. In Hong Kong, for example, the elderly care homes report a 70% shortage of employees. To alleviate these issues, recently there is a lot of attention on &lquot;data science for social goods&rquot;, or the use of technologies for enhancing service quality and streamlining administrative work of social workers. In this talk, I will discuss how the HKU STAR (Social Technology And Research) Lab uses data science technologies to support elderly and family care services. I will first introduce HINCare, a software platform that provides volunteering and cultivating mutual-help culture in the community. HINCare uses the HIN (Heterogeneous Information Network) to recommend helpers to elders or other service recipients, and is now supporting 14 NGOs and 7,000 users. I will also discuss our collaboration with the Hong Kong Jockey Club Charities Trust for developing a novel case management and data analysis system for 40% of the family care centers in Hong Kong. These projects have received an HKICT Award, Asia Smart App Awards, and HKU Faculty Knowledge Exchange Awards.

Speaker Bio:

Prof. Reynold Cheng Professor Reynold Cheng is a Professor of the Department of Computer Science and Associate Dean of Engineering of HKU. He is also an academic advisor to the College of Professional and Continuing Education of HKPU. His research interests are in data science, big graph analytics and uncertain data management. He received his BEng (Computer Engineering) in 1998 and MPhil in 2000 from HKU. He then obtained his MSc and PhD degrees from Department of Computer Science of Purdue University in 2003 and 2005.

Professor Cheng received the ACM Distinguished Membership Award and the HKU Outstanding Research Student Supervisor Award in 2023. He was listed as the World’s Top 2% Scientists by Stanford University in 2022, and is named the 2024 and 2023 AI 2000 Most Influential Scholar Honorable Mention in Database. He received the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021 and 2023, and HKU Knowledge Exchange Award (Engineering) 2021. He was granted an Outstanding Young Researcher Award 2011-12 by HKU. He received the Universitas 21 Fellowship in 2011, and two Performance Awards from HKPU Computing in 2006 and 2007. He is a member of IEEE, ACM, ACM SIGMOD, and UPE. He was a PC co-chair of IEEE ICDE 2021, and has been serving on the program committees and review panels for leading database conferences and journals like SIGMOD, VLDB, ICDE, KDD, IJCAI, AAAI, and TODS. He is on the editorial board of KAIS, IS and DAPD, and was a former editorial board member of TKDE.

Panel on Knowledge Graphs and LLMs: A Relationship under Investigation
moderated by Georgia Koutrika

Topic:

Graphs have attracted considerable attention from the community for their flexibility in modelling, organizing, managing, and querying heterogeneous data. In particular, knowledge graphs — structured representations of real-world entities and semantic relationships between them — offer enormous potential to meet data management and integration challenges. However, in practice, constructing high-quality knowledge graphs through manual means is infeasible, while labeling training data for machine learning systems requires substantial human effort. On the other hand, recently, LLMs have shown their ability to understand language, semantic connections, and generate synthesized content. For example, LLMs can extract structured knowledge graphs from unstructured document collections, and can recognize entities, types, and relationships in structured data. This creates a wealth of opportunities from knowledge graph construction and combining LLMs with knowledge graphs to even considering replacing KGs with powerful LLMs. In this panel, we aim to investigate this relationship between LLMs and KGs and discuss opportunities, perils and research directions for the future of data search, exploration, and analysis.

Speakers:
  • Prof. Reynold C. K. Cheng
  • Immanuel Trummer
  • George Fletcher

Submission Guidelines

All submissions will be electronic via the Easychair submission system.

Regular research papers as well as system papers have a page limit of 6 pages (references included).

Experiments and Analysis papers have also a page limit of 6 pages (references included).

Vision papers, work-in-progress papers, and experiences papers have a page limit of 4 pages (references included).

SEA Data workshop 2024 is single-blind, and thus authors must include their names and affiliations in submissions.

Formatting

Submitted papers must follow the ICDE Proceedings rules and must be prepared in accordance with the IEEE format as PDF files.

The font size, margins, inter-column spacing, and line spacing in the templates must be kept unchanged.

Any submitted paper violating the length, file type, or formatting requirements will be rejected without review.

Formatting guidelines for camera ready will follow.