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Matteo Lissandrini <[log in to unmask]>
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Sat, 23 Dec 2023 05:51:48 -0500
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SEA Graph 2024
3nd Workshop Search, Exploration, and Analysis in Heterogeneous Datastores
Graph Edition

Held in conjunction with  ICDE 2024 

** NEW: We set up a new deadline (January 15 2024) for the paper submission

** NEW: website URL: 

** NEW: Keynote Announcement by Prof. Reynold C.K. Cheng

IMPORTANT DATES (all deadlines are AoE): 

Submission: January 15, 2024
Author notification: February 20, 2024
Camera-ready: March 1, 2024
Workshop: May 13, 2024


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. 

* regular research and system papers (up to 6 pages)
* experiments and analysis paper (up to 6 pages)
* vision & work-in-progress papers (up to 4 pages)



* 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. 


    Davide Mottin, Aarhus University
    Matteo Lissandrini, University of Verona
    Lisa Ehrlinger, Software Competence Center Hagenberg GmbH
    Sayan Ranu, IIT Deli

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


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% o
 f 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 is a Professor of the Department of Computer Science, an Associate Dean of Engineering, and an Associate Director of the Musketeers Foundation Institute of Data Science in the University of Hong Kong (HKU). His research interests are in data science, big graph analytics and uncertain data management. He was the Assistant Professor in the Department of Computing of the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. He received his BEng (Computer Engineering) in 1998, and MPhil (Computer Science and Information Systems) in 2000 from HKU. He then obtained his MSc and PhD degrees from Department of Computer Science of Purdue University in 2003 and 2005. 


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