As enterprise data grows in size and complexity, column-level data lineage, which records the creation, transformation, and reference of each column in the warehouse, has been the key to effective data governance that assists tasks like data quality monitoring, storage refactoring, and workflow migration. Unfortunately, existing systems introduce overheads by integration with query execution or fail to achieve satisfying accuracy for column lineage. In this paper, we demonstrate LineageX, a lightweight Python library that infers column-level lineage from SQL queries and visualizes it through an interactive interface. LineageX achieves high coverage and accuracy for column lineage extraction by intelligently traversing query parse trees and handling ambiguities. The demonstration walks through use cases of building lineage graphs and troubleshooting data quality issues. LineageX is open sourced at https://github.com/sfu-db/lineagex and our video demonstration is at https://youtu.be/5LaBBDDitlw

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Deciding query equivalence has played an essential role in many real-world applications, including evaluating the accuracy of text-to-SQL models, where one needs to compare model-generated queries against ground truth queries. Although query equivalence is undecidable in general, researchers have developed two significant approaches to check query equivalence: formal verification-based and test-case-based. Verification-based solutions ensure correctness but may lack support for advanced SQL features and cross-database adaptability. Test cases are versatile but suffer from ad-hoc constraints and potential incorrectness (false positives). In this paper, we propose ParSEval, a Plan-aware SQL Equivalence evaluation framework to generate test database instances for given queries. We observed that existing test data generation methods fail to fully explore the query structure. To address this limitation, ParSEval formally models specific behaviors of each query operator and considers all possible execution paths of the logical query plan by adapting the notion of branch coverage. We validated the effectiveness and efficiency of ParSEval on four datasets with AI-generated and human-crafted queries. The experimental results show that ParSEval supports up to 40% more query pairs than state-of-the-art verification-based approaches. Compared to existing test-case-based approaches, ParSEval reveals more non-equivalent pairs while being 21× faster.

Entity matching is a crucial task in many real applications. Despite the substantial body of research that focuses on improving the effectiveness of entity matching, enhancing its fairness has received scant attention. To fill this gap, this paper introduces a new problem of preparing fairness-aware datasets for entity matching. We formally outline the problem, drawing upon the principles of group fairness and statistical parity. We devise three highly efficient algorithms to accelerate the process of identifying an unbiased dataset from the vast search space. Our experiments on four real-world datasets show that our proposed algorithms can significantly improve fairness in the results while achieving comparable effectiveness to existing fairness-agnostic methods. Furthermore, we conduct case studies to demonstrate that our proposed techniques can be seamlessly integrated into end-to-end entity matching pipelines to support fairness requirements in real-world applications.

Relational Web tables provide valuable resources for numerous downstream applications, making table understanding, especially column annotation that identifies semantic types and relations of columns, a hot topic in the field of data management. Despite recent efforts to improve different tasks in table understanding by using the power of large pre-trained language models, existing methods heavily rely on large-scale and high-quality labeled instances, while they still suffer from the data sparsity problem due to the imbalanced data distribution among different classes. In this paper, we propose the Watchog framework, which employs contrastive learning techniques to learn robust representations for tables by leveraging a large-scale unlabeled table corpus with minimal overhead. Our approach enables the learned table representations to enhance fine tuning with much fewer additional labeled instances than in prior studies for downstream column annotation tasks. Besides, we further proposed optimization techniques for semi-supervised settings. Experimental results on popular benchmarking datasets illustrate the superiority of our proposed techniques in two column annotation tasks under different settings. In particular, our Watchog framework effectively alleviates the class imbalance issue caused by a long-tailed label distribution. In the semi-supervised setting, Watchog outperforms the best-known method by up to 26% and 41% in Micro and Macro F1 scores, respectively, on the task of semantic type detection.

A powerful way to understand a complex query is by observing how it operates on data instances. However, specific database instances are not ideal for such observations: they often include large amounts of superfluous details that are not only irrelevant to understanding the query but also cause cognitive overload; and one specific database may not be enough. Given a relational query, is it possible to provide a simple and generic "representative'' instance that (1) illustrates how the query can be satisfied, (2) summarizes all specific instances that would satisfy the query in the same way by abstracting away unnecessary details? Furthermore, is it possible to find a collection of such representative instances that together completely characterize all possible ways in which the query can be satisfied? This paper takes initial steps towards answering these questions. We design what these representative instances look like, define what they stand for, and formalize what it means for them to satisfy a query in "all possible ways." We argue that this problem is undecidable for general domain relational calculus queries, and develop practical algorithms for computing a minimum collection of such instances subject to other constraints. We evaluate the efficiency of our approach experimentally, and show its effectiveness in helping users debug relational queries through a user study.

In this work, we demonstrate CaJaDE (Context-Aware Join-Augmented Deep Explanations), a system that explains query results by augmenting provenance with contextual information from other related tables in the database. Given two query results whose difference the user wants to understand, we enumerate possible ways of joining the provenance (i.e., contributing input tuples) of these two query results with tuples from other relevant tables in the database that were not used in the query. We use patterns to concisely explain the difference between the augmented provenance of the two query results. CaJaDE, through a comprehensive UI, enables the user to formulate questions and explore explanations interactively.

In many data analysis applications there is a need to explain why a surprising or interesting result was produced by a query. Previous approaches to explaining results have directly or indirectly relied on data provenance, i.e., input tuples contributing to the result(s) of interest. However, some information that is relevant for explaining an answer may not be contained in the provenance. We propose a new approach for explaining query results by augmenting provenance with information from other related tables in the database. Using a suite of optimization techniques, we demonstrate experimentally using real datasets and through a user study that our approach produces meaningful results and is efficient.

Deep Learning revolutionizes almost all fields of computer science including data management. However, the demand for high-quality training data is slowing down deep neural nets' wider adoption. To this end, data augmentation (DA), which generates more labeled examples from existing ones, becomes a common technique. Meanwhile, the risk of creating noisy examples and the large space of hyper-parameters make DA less attractive in practice. We introduce Rotom, a multi-purpose data augmentation framework for a range of data management and mining tasks including entity matching, data cleaning, and text classification. Rotom features InvDA, a new DA operator that generates natural yet diverse augmented examples by formulating DA as a seq2seq task. The key technical novelty of Rotom is a meta-learning framework that automatically learns a policy for combining examples from different DA operators, whereby combinatorially reduces the hyper-parameters space. Our experimental results show that Rotom effectively improves a model's performance by combining multiple DA operators, even when applying them individually does not yield performance improvement. With this strength, Rotom outperforms the state-of-the-art entity matching and data cleaning systems in the low-resource settings as well as two recently proposed DA techniques for text classification.

In recent years, we have witnessed the development of novel data augmentation (DA) techniques for creating additional training data needed by machine learning based solutions. In this tutorial, we will provide a comprehensive overview of techniques developed by the data management community for data preparation and data integration. In addition to surveying task-specific DA operators that leverage rules, transformations, and external knowledge for creating additional training data, we also explore the advanced DA techniques such as interpolation, conditional generation, and DA policy learning. Finally, we describe the connection between DA and other machine learning paradigms such as active learning, pre-training, and weakly-supervised learning. We hope that this discussion can shed light on future research directions for a holistic data augmentation framework for high-quality dataset creation.

Online services are interested in solutions to opinion mining, which is the problem of extracting aspects, opinions, and sentiments from text. One method to mine opinions is to leverage the recent success of pre-trained language models which can be fine-tuned to obtain high-quality extractions from reviews. However, fine-tuning language models still requires a non-trivial amount of training data.

We demonstrate I-Rex 1 , a system designed to help users understand SQL query evaluation and debug SQL queries. I-Rex lets users interactively "trace" the evaluation of complex SQL queries, including those with correlated subqueries. I-Rex also explains why a query returns an incorrect answer with respect to a reference query over a test database instance---a common use case in education and software regression testing. To avoid the cognitive overload caused by debugging over a large database instance, I-Rex lets users focus on smaller instances contained in the large one (which we call "counterexamples") that still distinguish the two queries. Supporting these features for SQL queries poses two key challenges. First, unlike debugging for procedural languages, it is not clear how to trace a declarative SQL query, because its execution plan often differs from how it was originally written. I-Rex offers a novel interface for tracing SQL query evaluation in a way faithful to how queries are written syntactically, even for complex queries involving multiple levels of nesting and correlation. Second, we need a method for finding small counterexamples that handles the complexity of practical SQL. I-Rex extends provenance support for SQL in non-trivial ways to work with various query constructs. This demonstration walks through use cases in which I-Rex helps users understand and debug SQL queries.

For testing the correctness of SQL queries, a standard practice is to execute the query in question on some test database instance and compare its result with that of the correct query. Given two queries $Q_1$ and $Q_2$, we say that a database instance D is a counterexample (for $Q_1$ and $Q_2$) if $Q_1(D)$ differs from $Q_2(D)$; such a counterexample can serve as an explanation of why $Q_1$ and $Q_2$ are not equivalent. While the test database instance may serve as a counterexample, it may be too large or complex to understand where the inequivalence arises. Therefore, in this paper, given a known counterexample D for $Q_1$ and $Q_2$, we aim to find the smallest counterexample $D' \subseteq D$ where $Q_1(D') \neq Q_2(D')$. The problem in general is NP-hard. Drawing techniques from provenance and constraint solving, we develop a suite of algorithms for finding small counterexamples for different classes of queries, including those involving negation and aggregation. We evaluate the effectiveness and scalability of our algorithms on student queries from an undergraduate database course, and on queries from the TPC-H benchmark. We also report a user study from the course where we deployed our tool to help students with an assignment on relational algebra.

We present a system called RATest, designed to help debug relational queries against reference queries and test database instances. In many applications, e.g., classroom learning and regression testing, we test the correctness of a user query Q by evaluating it over a test database instance D and comparing its result with that of evaluating a reference (correct) query $Q_0$ over D. If $Q(D)$ differs from $Q_0(D)$, the user knows Q is incorrect. However, D can be large (often by design), which makes debugging Q difficult. The key idea behind RATest is to show the user a much smaller database instance $D' \subseteq D$, which we call a counterexample, such that $Q(D') \neq Q_0(D')$. RATest builds on data provenance and constraint solving, and employs a suite of techniques to support, at interactive speed, complex queries involving differences and group-by aggregation. We demonstrate an application of RATest in learning: it has been used successfully by a large undergraduate database course in a university to help students with a relational algebra assignment.

Provenance and intervention-based techniques have been used to explain surprisingly high or low outcomes of aggregation queries. However, such techniques may miss interesting explanations emerging from data that is not in the provenance. For instance, an unusually low number of publications of a prolific researcher in a certain venue and year can be explained by an increased number of publications in another venue in the same year. We present a novel approach for explaining outliers in aggregation queries through counter- balancing. That is, explanations are outliers in the opposite direction of the outlier of interest. Outliers are defined w.r.t. patterns that hold over the data in aggregate. We present efficient methods for mining such aggregate regression pat- terns (ARPs), discuss how to use ARPs to generate and rank explanations, and experimentally demonstrate the efficiency and effectiveness of our approach.

In this demonstration, we will present LensXPlain, an interactive system to help users understand answers of aggregate queries by providing meaningful explanations. Given a SQL group-by query and a question from a user " why output o is high/low ", or " why output o 1 is higher/lower than o 2 ", LensXPlain helps users explore the results and find subsets of tuples captured by predicates that contributed the most toward such observations. The contributions are measured either by intervention (if the contributing tuples are removed, the values or the ratios in the user question change in the opposite direction), or by aggravation (if the query is restricted to the contributing tuples, the observations change more in the same direction). LensXPlain uses ensemble learning for recommending useful attributes in explanations, and employs a suite of optimizations to enable explanation generation and refinement at an interactive speed. In the demonstration, the audience can run aggregation queries over real world datasets, browse the answers using a graphical user interface, ask questions on unexpected/interesting query results with simple visualizations, and explore and refine explanations returned by LensXPlain.

In this demonstration we showcase Cape, a system that explains surprising aggregation outcomes. In contrast to previous work, which relies exclusively on provenance, Cape explains outliers in aggregation queries through related outliers in the opposite direction that provide counterbalance . The foundation of our approach are aggregate regression patterns (ARPs) that describe coarse-grained trends in the data. We define outliers as deviations from such patterns and present an efficient algorithm to find counterbalances explaining outliers. In the demonstration, the audience can run aggregation queries over real world datasets, identify outliers of interest in the result of such queries, and browse the patterns and explanations returned by Cape.