The proliferation of large language models (LLMs) has catalyzed a diverse array of applications. This tutorial delves into the application of LLMs for tabular data and targets a variety of table-related tasks, such as table understanding, text-to-SQL conversion, and tabular data preprocessing. It surveys LLM solutions to these tasks in five classes, categorized by their underpinning techniques: prompting, fine-tuning, RAG, agents, and multimodal methods. It discusses how LLMs offer innovative ways to interpret, augment, query, and cleanse tabular data, featuring academic contributions and their practical use in the industrial sector. It emphasizes the versatility and effectiveness of LLMs in handling complex table tasks, showcasing their ability to improve data quality, enhance analytical capabilities, and facilitate more intuitive data interactions. By surveying different approaches, this tutorial highlights the strengths of LLMs in enriching table tasks with more accuracy and usability, setting a foundation for future research and application in data science and AI-driven analytics. Presentation slides for this tutorial will be available at: https://dongyuyang.github.io/tableLLM-tutorial/ .

Due to the usefulness in data enrichment for data analysis tasks, joinable table discovery has become an important operation in data lake management. Existing approaches target equi-joins, the most common way of combining tables for creating a unified view, or semantic joins, which tolerate misspellings and different formats to deliver more join results. They are either exact solutions whose running time is linear in the sizes of query column and target table repository, or approximate solutions lacking precision. In this paper, we propose DeepJoin, a deep learning model for accurate and efficient joinable table discovery. Our solution is an embedding-based retrieval, which employs a pre-trained language model (PLM) and is designed as one framework serving both equi- and semantic (with a similarity condition on word embeddings) joins for textual attributes with fairly small cardinalities. We propose a set of contextualization options to transform column contents to a text sequence. The PLM reads the sequence and is fine-tuned to embed columns to vectors such that columns are expected to be joinable if they are close to each other in the vector space. Since the output of the PLM is fixed in length, the subsequent search procedure becomes independent of the column size. With a state-of-the-art approximate nearest neighbor search algorithm, the search time is sublinear in the repository size. To train the model, we devise the techniques for preparing training data as well as data augmentation. The experiments on real datasets demonstrate that by training on a small subset of a corpus, DeepJoin generalizes to large datasets and its precision consistently outperforms other approximate solutions'. DeepJoin is even more accurate than an exact solution to semantic joins when evaluated with labels from experts. Moreover, when equipped with a GPU, DeepJoin is up to two orders of magnitude faster than existing solutions.

Data scientists are constantly facing the problem of how to improve prediction accuracy with insufficient tabular data. We propose a table enrichment system that enriches a query table by adding external attributes (columns) from data lakes and improves the accuracy of machine learning predictive models. Our system has four stages, join row search, task-related table selection, row and column alignment, and feature selection and evaluation, to efficiently create an enriched table for a given query table and a specified machine learning task. We demonstrate our system with a web UI to show the use cases of table enrichment.

Finding joinable tables in data lakes is key procedure in many applications such as data integration, data augmentation, data analysis, and data market. Traditional approaches that find equi-joinable tables are unable to deal with misspellings and different formats, nor do they capture any semantic joins. In this paper, we propose PEXESO, a framework for joinable table discovery in data lakes. We target the case when textual values are embedded as high-dimensional vectors and columns are joined upon similarity predicates on high-dimensional vectors, hence to address the limitations of equi-join approaches and identify more meaningful results. To efficiently find joinable tables with similarity, we propose a block-and-verify method that utilizes pivot-based filtering. A partitioning technique is developed to cope with the case when the data lake is large and cannot fit in main memory. An experimental evaluation on real datasets shows that our solution identifies substantially more tables than equi-joins and outperforms other similarity-based options, and the join results are useful in data enrichment for machine learning tasks. The experiments also demonstrate the efficiency of the proposed method.

As the popularity of SNS and the number of GPS-equipped mobile devices increases, a large number of web users frequently change their location (spatial attribute) and interesting keywords (keyword attribute) in real-time. An example of such would be when a user watches the news, videos, and blogs while moving. Many location-based web applications can benefit from continuously searching for these dynamic spatial keyword objects. In this paper, we define a novel query problem to continuously search for dynamic spatial keyword objects. To the best of our knowledge, this is the first work to consider dynamic spatial keyword objects. We employ a novel grid-based index to manage both queries and dynamic spatial keyword objects. With the proposed index, we develop a buffer named partial cell list to reduce the computation cost in the top-k reevaluation. The experiments confirm the superiorities of our proposed methods.