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Data integration is an important step in any data science pipeline where the objective is to unify the information available in differ- ent datasets for comprehensive analysis. Full Disjunction, which is an associative extension of the outer join operator, has been shown to be an effective operator for integrating datasets. It fully preserves and combines the available information. Existing Full Disjunction algorithms only consider the equi-join scenario where only tuples having the same value on joining columns are integrated. This, however, does not realistically represent many realistic scenarios where datasets come from diverse sources with inconsistent values (e.g., synonyms, abbreviations, etc.) and with limited metadata. So, joining just on equal values severely limits the ability of Full Disjunction to fully combine datasets. Thus, in this work, we propose an extension of Full Disjunction to also account for “fuzzy” matches among tuples. We present a novel data-driven approach to enable the joining of approximate or fuzzy matches within Full Disjunction. Experimentally, we show that fuzzy Full Disjunction does not add significant time over- head over a state-of-the-art Full Disjunction implementation and also that it enhances the accuracy of a downstream data quality task. 

Unionable table search techniques input a query table from a user and search for data lake tables that can contribute additional rows to the query table. The definition of unionability is gener- ally based on similarity measures which may include similarity between columns (e.g., value overlap or semantic similarity of the values in the columns) or tables (e.g., similarity of table embed- dings). Due to this and the large redundancy in many data lakes (which can contain many copies and versions of the same table), the most unionable tables may be identical or nearly identical to the query table and may contain little new information. Hence, we introduce the problem of identifying unionable tuples from a data lake that are diverse with respect to the tuples already present in a query table. We perform an extensive experimen- tal analysis of well-known diversity algorithms applied to this novel problem and identify a gap that we address with a novel, clustering-based tuple diversity algorithm called DUST. DUST uses a novel embedding model to represent unionable tuples that outperforms other tuple representation models by at least 15% when representing unionable tuples. Using real data lake bench- marks, we show that our diversification algorithm is more than six times faster than the most efficient diversification baseline. We also show that it is more effective in diversifying unionable tuples than existing diversification algorithms. 

Ordinal regression classifies an object to a class out of a given set of possible classes, where labels possess a natural order. It is relevant to a wide array of domains including risk assessment, sentiment analysis, image ranking, and recommender systems. Like common classification, the primary goal of ordinal regression is accuracy. Yet, in this context, the severity of prediction errors varies, e.g., in risk assessment, Critical Risk is more urgent than High risk and significantly more urgent than No risk. This leads to a modified objective of ensuring that the model's output is as close as possible to the correct class, considering the order of labels. Therefore, ordinal regression models should use ordinality-aware loss for training. In this work, we focus on two properties of ordinality-aware losses, namely monotonicity and balance sensitivity. We show that existing ordinal loss functions lack these properties and introduce SLACE (Soft Labels Accumulating Cross Entropy), a novel loss function that provably satisfies said properties. We demonstrate empirically that SLACE outperforms the state-of-the-art ordinal loss functions on most tabular ordinal regression benchmarks. 

With the emerging advancements of AI, validating data generated by AI models becomes a key challenge. In this work, we tackle the problem of validating tabular data generated by large language models (LLMs). By leveraging a recently proposed technique called Gen-T, we present a technique to verify if the data in the LLM table can be reclaimed (reproduced) using tables available in a given data lake (for example, tables used to train the LLM). Specically, we measure the number of data lake tables that support tuples (or partial tuples) in a generated table. We further provide suggestions for value replacements if a generated value cannot be reclaimed. Using this approach, users can evaluate their LLM-generated tables, consider potential modications for table values, and gauge how much support the modied table has from the data lake. We discuss two case studies showing that table values annotated with reclama- tion support scores, along with possible value replacements, can help users assess the trustworthiness of LLM-generated tables. 

We consider the table union search problem which has emerged as an important data discovery problem in data lakes. Semantic problems like table union search cannot be benchmarked using only synthetic data. Our current methods for creating benchmarks for this problem involve the manual curation and human label- ing of real data. These methods are not robust or scalable and perhaps more importantly, it is not clear how comprehensive the created benchmarks are. We propose to use generative AI models to create structured data benchmarks for table union search. We present a novel method for using generative models to create ta- bles with specied properties. Using this method, we create a new benchmark containing pairs of tables that are both unionable and non-unionable, but related. We use this benchmark to provide new insights into the strengths and weaknesses of existing methods. We evaluate state-of-the-art table union search methods over both existing benchmarks and our new benchmarks. We also present and evaluate a new table search method based on large language models over all benchmarks. We show that the new benchmarks are more challenging for all methods than hand-curated benchmarks. We examine why this is the case and show that our new methodology for creating benchmarks permits more detailed analysis and com- parison of methods. We discuss how our generation method (and benchmarks created using it) sheds more light into the successes and failures of table union search methods sparking new insights that can help advance the eld. We also discuss how our benchmark generation methodology can be applied to other semantic problems including entity matching and related table search. 

We introduce the problem of Table Reclamation. Given a Source Table and a large table repository, reclamation finds a set of tables that, when integrated, reproduce the source table as closely as possible. Unlike query discovery problems like Query-by-Example or by-Target, Table Reclamation focuses on reclaiming the data in the Source Table as fully as possible using real tables that may be incomplete or inconsistent. To do this, we define a new measure of table similarity, called error-aware instance similarity, to measure how close a reclaimed table is to a Source Table, a measure grounded in instance similarity used in data exchange. Our search covers not only SELECT-PROJECT- JOIN queries, but integration queries with unions, outerjoins, and the unary operators subsumption and complementation that have been shown to be important in data integration and fusion. Using reclamation, a data scientist can understand if any tables in a repository can be used to exactly reclaim a tuple in the Source. If not, one can understand if this is due to differences in values or to incompleteness in the data. Our solution, Gen- T, performs table discovery to retrieve a set of candidate tables from the table repository, filters these down to a set of originating tables, then integrates these tables to reclaim the Source as closely as possible. We show that our solution, while approximate, is accurate, efficient and scalable in the size of the table repository with experiments on real data lakes containing up to 15K tables, where the average number of tuples varies from small (web tables) to extremely large (open data tables) up to 1M tuples.