Objectives
Data Analysis is described as the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Performing such 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 this workshop 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 two communities: 1) an active community of data management researchers working on data integration and schema and data matching problems over tabular data, and 2) 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, use of large language models (LLMs) for tabular data analysis, andautomation of data science pipelines, and automation of data science pipelines that rely on tabular data. c) discussion of real-world challenges related to implementing industrial-scale tabular data anaylsis pipelines, and data lakes and data lakehouse solutions.