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H. V. Jagadish

Publications and source records attributed to H. V. Jagadish.

At least 19 recordsLinked to original sources
arXiv2026

From Research Questions to Columns: Operationalization-Aware Data Discovery

Researchers often approach a data repository with an abstract concept and ask which columns can measure it. Useful columns may not resemble the query; they may matter only as complementary indicators in a defensible measure. This need differs from schema linking and column retrieval, which begin from more explicit needs and reward direct relevance. We define operationalization-aware data discovery (OADD): given a broad question and a database, optionally under a scope constraint, OADD jointly determines how focal concepts can be measured with available data and identifies supporting columns. Developing OADD methods requires examples for design and evaluation, but asking researchers to supply conceptual questions and their columns is impractical. We construct OADD-Bench by treating empirical papers as records of schema in use. A question miner extracts and reframes a paper-supported question; a paper-conditioned column miner reconstructs its measurements and grounds them to database identifiers. We admit only mappings supported by the publication and database documentation. OADD-Bench contains 160 questions from 111 papers and 4,682 question-column labels. Each target records a measurement used in published research; the paper supplies the precedent, while the miners extract and ground it. We evaluate lexical and neural retrieval, adapted schema-linking systems, and large language model (LLM) OADD agents. Each method receives only a question, permitted years, and dataset metadata; source papers are used only to construct and document benchmark labels. At the largest output limit, direct retrieval reaches at most 0.185 recall. The strongest schema-linking adaptation reaches 0.401 but remains optimized for a different objective; an OADD-directed agent performs best at 0.465. Even this agent covers less than half the target columns, showing that OADD remains an open problem.

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arXiv2026

The General Stability of Ranking

Rankings derived from weighted scoring functions are widely used in settings such as university rankings and employment candidate evaluations. Since ranking weights are often chosen by organizations or analysts, ranking stability asks whether a reported ranking persists under reasonable weight changes. Prior work on stable rankings formalizes this idea through volume-based stability, which measures the fraction of the weight space that induces the target ranking exactly. This exact-match requirement can be too blunt: once a perturbed weight vector produces a different ranking, exact stability gives it no credit, whether the change replaces the top-ranked item or only swaps two nearly tied lower-ranked items. We propose general stability, a distance-based generalization that aggregates ranking regions according to a user-defined distance from the target ranking. This lets users specify which ranking changes matter in the application, while recovering exact stability as a special case. Our algorithmic focus is stability computation: given a reported or user-specified ranking and a distance function, estimate its general-stability score. We give a two-dimensional sweep algorithm and an unbiased multidimensional sampler that extend exact-stability methods, and analyze why sampling can scale poorly as the dimension grows. Motivated by this scaling challenge, we identify quasiconvex distance functions as a tractable subclass and introduce Conv-SC, which reduces stability computation for this subclass to convex-volume approximation, where randomized polynomial-time methods are available. Experiments on eight real datasets and generated instances show that distance-sensitive stability gives informative real-data results, that our estimators are accurate and practical, and that Conv-SC improves scaling with dimension for quasiconvex distance functions.

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arXiv2026

SEMA-SQL: Beyond Traditional Relational Querying with Large Language Models

Relational databases excel at structured data analysis, but real-world queries increasingly require capabilities beyond standard SQL, such as semantically matching entities across inconsistent names, extracting information not explicitly stored in schemas, and analyzing unstructured text. While text-to-SQL systems enable natural language querying, they remain limited to relational operations and cannot leverage the semantic reasoning capabilities of modern large language models (LLMs). Conversely, recent semantic operator systems extend relational algebra with LLM-powered operations (e.g., semantic joins, mappings, aggregations), but require users to manually construct complex query pipelines. To address this gap, we present SEMA-SQL, a system that automatically answers natural language questions by generating efficient queries that combine relational operations with LLM semantic reasoning. We formalize Hybrid Relational Algebra (HRA), a declarative abstraction unifying traditional relational operators with LLM user-defined functions (UDFs). The system automates three critical aspects: (1) query generation via in-context learning that produces HRA queries with precise natural language specifications for LLM UDFs, (2) query optimization via cost-based transformations and UDF rewriting, and (3) efficient execution algorithms that reduce LLM invocations by an average of 93% in semantic joins through intelligent batching. Extensive experiments with known benchmarks, and extensions thereof, demonstrate the significant query capability improvements possible with our design.

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arXiv2026

ConStruM: A Structure-Guided LLM Framework for Context-Aware Schema Matching

Column matching is a central task in reconciling schemas for data integration. Column names and descriptions are valuable for this task. LLMs can leverage such natural-language schema metadata. However, in many datasets, correct matching requires additional evidence beyond the column itself. Because it is impractical to provide an LLM with the entire schema metadata needed to capture this evidence, the core challenge becomes to select and organize the most useful contextual information. We present ConStruM, a structure-guided framework for budgeted evidence packing in schema matching. ConStruM constructs a lightweight, reusable structure in which, at query time, it assembles a small context pack emphasizing the most discriminative evidence. ConStruM is designed as an add-on: given a shortlist of candidate targets produced by an upstream matcher, it augments the matcher's final LLM prompt with structured, query-specific evidence so that the final selection is better grounded. For this purpose, we develop a context tree for budgeted multi-level context retrieval and a global similarity hypergraph that surfaces groups of highly similar columns (on both the source and target sides), summarized via group-aware differentiation cues computed online or precomputed offline. Experiments on real datasets show that ConStruM improves matching by providing and organizing the right contextual evidence.

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arXiv2026

SemBench: A Benchmark for Semantic Query Processing Engines

We present a benchmark targeting a novel class of systems: semantic query processing engines. Those systems rely inherently on generative and reasoning capabilities of state-of-the-art large language models (LLMs). They extend SQL with semantic operators, configured by natural language instructions, that are evaluated via LLMs and enable users to perform various operations on multimodal data. Our benchmark introduces diversity across three key dimensions: scenarios, modalities, and operators. Included are scenarios ranging from movie review analysis to car damage detection. Within these scenarios, we cover different data modalities, including images, audio, and text. Finally, the queries involve a diverse set of operators, including semantic filters, joins, mappings, ranking, and classification operators. We evaluated our benchmark on three academic systems (LOTUS, Palimpzest, and ThalamusDB) and one industrial system, Google BigQuery. Although these results reflect a snapshot of systems under continuous development, our study offers crucial insights into their current strengths and weaknesses, illuminating promising directions for future research.

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arXiv2026

MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark

Tables and table-based use cases play a crucial role in many important real-world applications, such as spreadsheets, databases, and computational notebooks, which traditionally require expert-level users like data engineers, data analysts, and database administrators to operate. Although LLMs have shown remarkable progress in working with tables (e.g., in spreadsheet and database copilot scenarios), comprehensive benchmarking of such capabilities remains limited. In contrast to an extensive and growing list of NLP benchmarks, evaluations of table-related tasks are scarce, and narrowly focus on tasks like NL-to-SQL and Table-QA, overlooking the broader spectrum of real-world tasks that professional users face. This gap limits our understanding and model progress in this important area. In this work, we introduce MMTU, a large-scale benchmark with over 28K questions across 25 real-world table tasks, designed to comprehensively evaluate models ability to understand, reason, and manipulate real tables at the expert-level. These tasks are drawn from decades' worth of computer science research on tabular data, with a focus on complex table tasks faced by professional users. We show that MMTU require a combination of skills -- including table understanding, reasoning, and coding -- that remain challenging for today's frontier models, where even frontier reasoning models like OpenAI GPT-5 and DeepSeek R1 score only around 69\% and 57\% respectively, suggesting significant room for improvement. We highlight key findings in our evaluation using MMTU and hope that this benchmark drives further advances in understanding and developing foundation models for structured data processing and analysis. Our code and data are available at https://github.com/MMTU-Benchmark/MMTU and https://huggingface.co/datasets/MMTU-benchmark/MMTU.

cs.AI↗
arXiv2026

Nexus: Inferring Join Graphs from Metadata Alone via Iterative Low-Rank Matrix Completion

Automatically inferring join relationships is a critical task for effective data discovery, integration, querying and reuse. However, accurately and efficiently identifying these relationships in large and complex schemas can be challenging, especially in enterprise settings where access to data values is constrained. In this paper, we introduce the problem of join graph inference when only metadata is available. We conduct an empirical study on a large number of real-world schemas and observe that join graphs when represented as adjacency matrices exhibit two key properties: high sparsity and low-rank structure. Based on these novel observations, we formulate join graph inference as a low-rank matrix completion problem and propose Nexus, an end-to-end solution using only metadata. To further enhance accuracy, we propose a novel Expectation-Maximization algorithm that alternates between low-rank matrix completion and refining join candidate probabilities by leveraging Large Language Models. Our extensive experiments demonstrate that Nexus outperforms existing methods by a significant margin on four datasets including a real-world production dataset. Additionally, Nexus can operate in a fast mode, providing comparable results with up to 6x speedup, offering a practical and efficient solution for real-world deployments.

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arXiv2024

SMARTFEAT: Efficient Feature Construction through Feature-Level Foundation Model Interactions

Before applying data analytics or machine learning to a data set, a vital step is usually the construction of an informative set of features from the data. In this paper, we present SMARTFEAT, an efficient automated feature engineering tool to assist data users, even non-experts, in constructing useful features. Leveraging the power of Foundation Models (FMs), our approach enables the creation of new features from the data, based on contextual information and open-world knowledge. Our method incorporates an intelligent operator selector that discerns a subset of operators, effectively avoiding exhaustive combinations of original features, as is typically observed in traditional automated feature engineering tools. Moreover, we address the limitations of performing data tasks through row-level interactions with FMs, which could lead to significant delays and costs due to excessive API calls. We introduce a function generator that facilitates the acquisition of efficient data transformations, such as dataframe built-in methods or lambda functions, ensuring the applicability of SMARTFEAT to generate new features for large datasets. Code repo with prompt details and datasets: (https://github.com/niceIrene/SMARTFEAT).

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arXiv2024

OpenForge: Probabilistic Metadata Integration

Modern data stores increasingly rely on metadata for enabling diverse activities such as data cataloging and search. However, metadata curation remains a labor-intensive task, and the broader challenge of metadata maintenance -- ensuring its consistency, usefulness, and freshness -- has been largely overlooked. In this work, we tackle the problem of resolving relationships among metadata concepts from disparate sources. These relationships are critical for creating clean, consistent, and up-to-date metadata repositories, and a central challenge for metadata integration. We propose OpenForge, a two-stage prior-posterior framework for metadata integration. In the first stage, OpenForge exploits multiple methods including fine-tuned large language models to obtain prior beliefs about concept relationships. In the second stage, OpenForge refines these predictions by leveraging Markov Random Field, a probabilistic graphical model. We formalize metadata integration as an optimization problem, where the objective is to identify the relationship assignments that maximize the joint probability of assignments. The MRF formulation allows OpenForge to capture prior beliefs while encoding critical relationship properties, such as transitivity, in probabilistic inference. Experiments on real-world datasets demonstrate the effectiveness and efficiency of OpenForge. On a use case of matching two metadata vocabularies, OpenForge outperforms GPT-4, the second-best method, by 25 F1-score points.

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arXiv2024

VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection

Fraud detection presents a challenging task characterized by ever-evolving fraud patterns and scarce labeled data. Existing methods predominantly rely on graph-based or sequence-based approaches. While graph-based approaches connect users through shared entities to capture structural information, they remain vulnerable to fraudsters who can disrupt or manipulate these connections. In contrast, sequence-based approaches analyze users' behavioral patterns, offering robustness against tampering but overlooking the interactions between similar users. Inspired by cohort analysis in retention and healthcare, this paper introduces VecAug, a novel cohort-augmented learning framework that addresses these challenges by enhancing the representation learning of target users with personalized cohort information. To this end, we first propose a vector burn-in technique for automatic cohort identification, which retrieves a task-specific cohort for each target user. Then, to fully exploit the cohort information, we introduce an attentive cohort aggregation technique for augmenting target user representations. To improve the robustness of such cohort augmentation, we also propose a novel label-aware cohort neighbor separation mechanism to distance negative cohort neighbors and calibrate the aggregated cohort information. By integrating this cohort information with target user representations, VecAug enhances the modeling capacity and generalization capabilities of the model to be augmented. Our framework is flexible and can be seamlessly integrated with existing fraud detection models. We deploy our framework on e-commerce platforms and evaluate it on three fraud detection datasets, and results show that VecAug improves the detection performance of base models by up to 2.48\% in AUC and 22.5\% in R@P$_{0.9}$, outperforming state-of-the-art methods significantly.

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arXiv2024

CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analytics

Cohort studies are of significant importance in the field of healthcare analysis. However, existing methods typically involve manual, labor-intensive, and expert-driven pattern definitions or rely on simplistic clustering techniques that lack medical relevance. Automating cohort studies with interpretable patterns has great potential to facilitate healthcare analysis but remains an unmet need in prior research efforts. In this paper, we propose a cohort auto-discovery model, CohortNet, for interpretable healthcare analysis, focusing on the effective identification, representation, and exploitation of cohorts characterized by medically meaningful patterns. CohortNet initially learns fine-grained patient representations by separately processing each feature, considering both individual feature trends and feature interactions at each time step. Subsequently, it classifies each feature into distinct states and employs a heuristic cohort exploration strategy to effectively discover substantial cohorts with concrete patterns. For each identified cohort, it learns comprehensive cohort representations with credible evidence through associated patient retrieval. Ultimately, given a new patient, CohortNet can leverage relevant cohorts with distinguished importance, which can provide a more holistic understanding of the patient's conditions. Extensive experiments on three real-world datasets demonstrate that it consistently outperforms state-of-the-art approaches and offers interpretable insights from diverse perspectives in a top-down fashion.

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arXiv2024

Enhanced Language Model Truthfulness with Learnable Intervention and Uncertainty Expression

Large language models (LLMs) can generate long-form and coherent text, yet they often hallucinate facts, which undermines their reliability. To mitigate this issue, inference-time methods steer LLM representations toward the "truthful directions" previously learned for truth elicitation. However, applying these truthful directions with the same intensity fails to generalize across different query contexts. We propose LITO, a Learnable Intervention method for Truthfulness Optimization that automatically identifies the optimal intervention intensity tailored to each specific context. LITO explores a sequence of model generations based on increasing levels of intervention intensities. It selects the most accurate response or refuses to answer when the predictions are highly uncertain. Experiments on multiple LLMs and question-answering datasets demonstrate that LITO improves truthfulness while preserving task accuracy. The adaptive nature of LITO counters the limitations of one-size-fits-all intervention methods, maximizing truthfulness by reflecting the model's internal knowledge only when it is confident. Our code is available at https://github.com/launchnlp/LITO.

cs.CL↗
arXiv2024

Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities

The potential harms of the under-representation of minorities in training data, particularly in multi-modal settings, is a well-recognized concern. While there has been extensive effort in detecting such under-representation, resolution has remained a challenge. With recent advancements in generative AI, large language models and foundation models have emerged as versatile tools across various domains. In this paper, we propose Chameleon, a system that efficiently utilizes these tools to augment a data set with a minimal addition of synthetically generated tuples, in order to enhance the coverage of the under-represented groups. Our system follows a rejection sampling approach to ensure the generated tuples have a high quality and follow the underlying distribution. In order to minimize the rejection chance of the generated tuples, we propose multiple strategies for providing a guide for the foundation model. Our experiment results, in addition to confirming the efficiency of our proposed algorithms, illustrate the effectiveness of our approach, as the unfairness of the model in a downstream task significantly dropped after data repair using Chameleon.

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arXiv2024

Observatory: Characterizing Embeddings of Relational Tables

Language models and specialized table embedding models have recently demonstrated strong performance on many tasks over tabular data. Researchers and practitioners are keen to leverage these models in many new application contexts; but limited understanding of the strengths and weaknesses of these models, and the table representations they generate, makes the process of finding a suitable model for a given task reliant on trial and error. There is an urgent need to gain a comprehensive understanding of these models to minimize inefficiency and failures in downstream usage. To address this need, we propose Observatory, a formal framework to systematically analyze embedding representations of relational tables. Motivated both by invariants of the relational data model and by statistical considerations regarding data distributions, we define eight primitive properties, and corresponding measures to quantitatively characterize table embeddings for these properties. Based on these properties, we define an extensible framework to evaluate language and table embedding models. We collect and synthesize a suite of datasets and use Observatory to analyze nine such models. Our analysis provides insights into the strengths and weaknesses of learned representations over tables. We find, for example, that some models are sensitive to table structure such as column order, that functional dependencies are rarely reflected in embeddings, and that specialized table embedding models have relatively lower sample fidelity. Such insights help researchers and practitioners better anticipate model behaviors and select appropriate models for their downstream tasks, while guiding researchers in the development of new models.

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arXiv2023

Detection of Groups with Biased Representation in Ranking

Real-life tools for decision-making in many critical domains are based on ranking results. With the increasing awareness of algorithmic fairness, recent works have presented measures for fairness in ranking. Many of those definitions consider the representation of different ``protected groups'', in the top-$k$ ranked items, for any reasonable $k$. Given the protected groups, confirming algorithmic fairness is a simple task. However, the groups' definitions may be unknown in advance. In this paper, we study the problem of detecting groups with biased representation in the top-$k$ ranked items, eliminating the need to pre-define protected groups. The number of such groups possible can be exponential, making the problem hard. We propose efficient search algorithms for two different fairness measures: global representation bounds, and proportional representation. Then we propose a method to explain the bias in the representations of groups utilizing the notion of Shapley values. We conclude with an experimental study, showing the scalability of our approach and demonstrating the usefulness of the proposed algorithms.

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arXiv2023

Representation Bias in Data: A Survey on Identification and Resolution Techniques

Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately. Representation Bias in data can happen due to various reasons ranging from historical discrimination to selection and sampling biases in the data acquisition and preparation methods. Given that "bias in, bias out", one cannot expect AI-based solutions to have equitable outcomes for societal applications, without addressing issues such as representation bias. While there has been extensive study of fairness in machine learning models, including several review papers, bias in the data has been less studied. This paper reviews the literature on identifying and resolving representation bias as a feature of a data set, independent of how consumed later. The scope of this survey is bounded to structured (tabular) and unstructured (e.g., image, text, graph) data. It presents taxonomies to categorize the studied techniques based on multiple design dimensions and provides a side-by-side comparison of their properties. There is still a long way to fully address representation bias issues in data. The authors hope that this survey motivates researchers to approach these challenges in the future by observing existing work within their respective domains.

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arXiv2023

Pylon: Semantic Table Union Search in Data Lakes

The large size and fast growth of data repositories, such as data lakes, has spurred the need for data discovery to help analysts find related data. The problem has become challenging as (i) a user typically does not know what datasets exist in an enormous data repository; and (ii) there is usually a lack of a unified data model to capture the interrelationships between heterogeneous datasets from disparate sources. In this work, we address one important class of discovery needs: finding union-able tables. The task is to find tables in a data lake that can be unioned with a given query table. The challenge is to recognize union-able columns even if they are represented differently. In this paper, we propose a data-driven learning approach: specifically, an unsupervised representation learning and embedding retrieval task. Our key idea is to exploit self-supervised contrastive learning to learn an embedding model that takes into account the indexing/search data structure and produces embeddings close by for columns with semantically similar values while pushing apart columns with semantically dissimilar values. We then find union-able tables based on similarities between their constituent columns in embedding space. On a real-world data lake, we demonstrate that our best-performing model achieves significant improvements in precision ($16\% \uparrow$), recall ($17\% \uparrow $), and query response time (7x faster) compared to the state-of-the-art.

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arXiv2023

WarpGate: A Semantic Join Discovery System for Cloud Data Warehouses

Data discovery is a major challenge in enterprise data analysis: users often struggle to find data relevant to their analysis goals or even to navigate through data across data sources, each of which may easily contain thousands of tables. One common user need is to discover tables joinable with a given table. This need is particularly critical because join is a ubiquitous operation in data analysis, and join paths are mostly obscure to users, especially across databases. Furthermore, users are typically interested in finding ``semantically'' joinable tables: with columns that can be transformed to become joinable even if they are not joinable as currently represented in the data store. We present WarpGate, a system prototype for data discovery over cloud data warehouses. WarpGate implements an embedding-based solution to semantic join discovery, which encodes columns into high-dimensional vector space such that joinable columns map to points that are near each other. Through experiments on several table corpora, we show that WarpGate (i) captures semantic relationships between tables, especially those across databases, and (ii) is sample efficient and thus scalable to very large tables of millions of rows. We also showcase an application of WarpGate within an enterprise product for cloud data analytics.

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