SemTab-related workshop at VLDB (TaDA @ VLDB 2023) - CfP
Vasilis Efthymiou
Call for Papers - TaDA @ VLDB 2023
Performing data analysis tasks over large and heterogeneous collections of tabular data, as found in enterprise data lakes and on the Web, is extremely challenging and an attractive research topic in data management, AI, and related communities. The goal of the Tabular Data Analysis (TaDA) workshop (https://tabular-data-analysis.github.io/tada2023/), co-located with VLDB 2023 on September 1, 2023, is to bring together researchers and practitioners in these diverse communities that work on addressing the fundamental research challenges of tabular data analysis and building automated solutions in this space.
We aim to provide a forum for: a) exchange of ideas between the following communities: i) an active community of data management researchers working on data integration and schema and data matching problems over tabular data, and ii) a vibrant community of researchers in AI and Semantic Web communities working on the core challenge of matching tabular data to Knowledge Graphs, as a part of the ISWC SemTab Challenges. b) presentation of late-breaking results related to several emerging research areas such as table representation learning and its applications, automation of data science pipelines, and data lake and data lakehouse solutions. c) discussion of real-world data management challenges related to implementing industrial scale tabular data analysis solutions.
Topics of Interest include but are not limited to:
Semantic Table Annotation
Automated Tabular Data Understanding
Exploratory Data Analysis over Tabular Data
Table Search in Data Lakes
Tabular Data Discovery
Tabular Data Discovery in Data Lakes
Tabular Data Discovery for Causal Inference
Metadata Management for Tabular Data Analysis
Data Augmentation with Tabular Data
Integration and Matching of Tabular Data
Knowledge Graph Construction and Completion with Tabular Data
Automated Discovery of ML Features from Tabular Data
ML Model Development with Tabular Data
Visualization and Interfaces for Tabular Data Analysis
Data Wrangling for Tabular Data Analysis
Deep Learning and Representation Learning for Tabular Data Analysis
Foundation Models for Tabular Data Analysis
Extraction and Analysis of Tabular Data from (HTML/PDF) Documents and Images
Analysis of Tabular Data on the Web (Web Tables)
Practical Applications of Tabular Data Analysis
Benchmarking and Evaluation Frameworks for Tabular Data Analysis
Submission Guidelines
Contributions to the workshop can take the form of technical papers, posters, or statements of interest addressing various aspects of tabular data analysis, as well as reports on SemTab Challenge (https://www.cs.ox.ac.uk/isg/challenges/sem-tab/) participation. Overall, we accept the following types of submissions:
Long technical papers: 8-10 pages
Short technical papers: up to 4 pages
Posters: up to 2 pages
References do not count towards the page limits mentioned above.
The submissions should be made to the workshop’s CMT site (https://cmt3.research.microsoft.com/TaDA2023) and follow the double-column CEUR-ART (https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-workshop-proceedings-ceur-ws-dot-org/wqyfdgftmcfw) template. Submissions will be single-blind.
Authors of accepted papers will have the option to include their papers in the CEUR-ART proceedings of the workshop. At least one co-author is expected to register for the VLDB 2023 conference and present the paper in-person.
Important Dates
Abstract submission deadline: May 8, 2023
Submission deadline: May 15, 2023
Notification of acceptance: June 23, 2023
Camera-ready copy due: July 20, 2023
Organizing Committee
Vasilis Efthymiou (FORTH), Sainyam Galhotra (UChicago), Oktie Hassanzadeh (IBM Research), Ernesto Jiménez-Ruiz (City, Un. of London), Kavitha Srinivas (IBM Research)
Steering Committee
Haoyu Dong (Microsoft), Shi Han (Microsoft), Madelon Hulsebos (University of Amsterdam), Chuan Lei (AWS), Fatemeh Nargesian (University of Rochester), Natasha Noy (Google), Horst Samulowitz (IBM Research)