arXiv subjects

Alexandra Meliou

Publications and source records attributed to Alexandra Meliou.

At least 19 recordsLinked to original sources

arXiv2026

Chimera: Efficient Multi-Vector Retrieval via GPU-CPU Co-Processing

Multi-vector retrieval has become an important primitive for fine-grained matching in information retrieval, with emerging applications in areas such as recommender systems and bioinformatics. However, its high computational complexity and memory costs make low-latency retrieval difficult. Prior systems have attempted to optimize query latency, but their designs remain CPU-centric. While GPUs offer substantial computational advantages, their limited memory capacity necessitates a heterogeneous architecture in which the dataset resides in host memory and the GPU serves as an accelerator. Existing GPU-based system, PLAID, is bottlenecked by CPU-GPU data movement, as vector data must be transferred from host memory to the GPU at query time. We propose Chimera, a GPU-CPU co-processing system for multi-vector retrieval that eliminates this transfer bottleneck. Chimera stores highly compressed, low-precision quantization codes on the GPU while maintaining high-precision data in CPU memory. At query time, it leverages GPU-resident data for efficient candidate generation and filtering, and further refines results through a GPU-CPU collaborative scoring scheme that completely avoids vector data transfer while enabling computation overlap. Experiments on real-world datasets demonstrate that Chimera significantly outperforms existing approaches, achieving up to 59.5x higher QPS at the same recall level.

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arXiv2026

Example-Driven Intent Synthesis for Constrained Data Bundle Retrieval: Focused Text Snippet Extraction and Beyond

Selecting a bundle of items that collectively satisfies constraints is a fundamental task across databases, recommender systems, and text summarization. Unlike traditional retrieval that returns individual or top-k items, bundle retrieval is inherently combinatorial and, in general, NP-hard. Although package queries can efficiently retrieve bundles given a well-formed query, two key user-centric challenges remain: (1) expressing and tuning multi-dimensional bundle intent through a user-friendly interface, and (2) ensuring feasibility when the query yields empty results. We introduce Ex2Bundle, an Example-driven Bundle retrieval framework that enables users to specify their intent through example bundles and automatically synthesizes package queries that capture the intent implicit in those example bundles via aggregate constraints. Ex2Bundle also addresses a challenge unique to bundle retrieval: when inferred aggregate constraints are infeasible over the target data, our data-aware constraint relaxation minimally adjusts the constraint bounds while preserving alignment with user intent. We instantiate a specific application of focused text snippet extraction by example to demonstrate the efficacy of the Ex2Bundle framework. Extensive experiments over real-world datasets and a user study demonstrate that Ex2Bundle improves usability and consistently returns intent-aligned bundles even under distributional shifts of the target database.

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arXiv2025

DiskJoin: Large-scale Vector Similarity Join with SSD

Similarity join--a widely used operation in data science--finds all pairs of items that have distance smaller than a threshold. Prior work has explored distributed computation methods to scale similarity join to large data volumes but these methods require a cluster deployment, and efficiency suffers from expensive inter-machine communication. On the other hand, disk-based solutions are more cost-effective by using a single machine and storing the large dataset on high-performance external storage, such as NVMe SSDs, but in these methods the disk I/O time is a serious bottleneck. In this paper, we propose DiskJoin, the first disk-based similarity join algorithm that can process billion-scale vector datasets efficiently on a single machine. DiskJoin improves disk I/O by tailoring the data access patterns to avoid repetitive accesses and read amplification. It also uses main memory as a dynamic cache and carefully manages cache eviction to improve cache hit rate and reduce disk retrieval time. For further acceleration, we adopt a probabilistic pruning technique that can effectively prune a large number of vector pairs from computation. Our evaluation on real-world, large-scale datasets shows that DiskJoin significantly outperforms alternatives, achieving speedups from 50x to 1000x.

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arXiv2025

Perception-aware Sampling for Scatterplot Visualizations

Visualizing data is often a crucial first step in data analytics workflows, but growing data sizes pose challenges due to computational and visual perception limitations. As a result, data analysts commonly down-sample their data and work with subsets. Deriving representative samples, however, remains a challenge. This paper focuses on scatterplots, a widely-used visualization type, and introduces a novel sampling objective -- perception-awareness -- aiming to improve sample efficacy by targeting humans' perception of a visualization. We make the following contributions: (1) We propose perception-augmented databases and design PAwS: a novel perception-aware sampling method for scatterplots that leverages saliency maps -- a computer vision tool for predicting areas of attention focus in visualizations -- and models perception-awareness via saliency, density, and coverage objectives. (2) We design ApproPAwS: a fast, perception-aware method for approximate visualizations, which exploits the fact that small visual perturbations are often imperceptible to humans. (3) We introduce the concept of perceptual similarity as a metric for sample quality, and present a novel method that compares saliency maps to measure it. (4) Our extensive experimental evaluation shows that our methods consistently outperform prior art in producing samples with high perceptual similarity, while ApproPAwS achieves up to 100x speed-ups with minimal loss in visual fidelity. Our user study shows that PAwS is often preferred by humans, validating our quantitative findings.

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arXiv2024

Leveraging Foundation Language Models (FLMs) for Automated Cohort Extraction from Large EHR Databases

A crucial step in cohort studies is to extract the required cohort from one or more study datasets. This step is time-consuming, especially when a researcher is presented with a dataset that they have not previously worked with. When the cohort has to be extracted from multiple datasets, cohort extraction can be extremely laborious. In this study, we present an approach for partially automating cohort extraction from multiple electronic health record (EHR) databases. We formulate the guided multi-dataset cohort extraction problem in which selection criteria are first converted into queries, translating them from natural language text to language that maps to database entities. Then, using FLMs, columns of interest identified from the queries are automatically matched between the study databases. Finally, the generated queries are run across all databases to extract the study cohort. We propose and evaluate an algorithm for automating column matching on two large, popular and publicly-accessible EHR databases -- MIMIC-III and eICU. Our approach achieves a high top-three accuracy of $92\%$, correctly matching $12$ out of the $13$ columns of interest, when using a small, pre-trained general purpose language model. Furthermore, this accuracy is maintained even as the search space (i.e., size of the database) increases.

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arXiv2024

Stochastic SketchRefine: Scaling In-Database Decision-Making under Uncertainty to Millions of Tuples

Decision making under uncertainty often requires choosing packages, or bags of tuples, that collectively optimize expected outcomes while limiting risks. Processing Stochastic Package Queries (SPQs) involves solving very large optimization problems on uncertain data. Monte Carlo methods create numerous scenarios, or sample realizations of the stochastic attributes of all the tuples, and generate packages with optimal objective values across these scenarios. The number of scenarios needed for accurate approximation - and hence the size of the optimization problem when using prior methods - increases with variance in the data, and the search space of the optimization problem increases exponentially with the number of tuples in the relation. Existing solvers take hours to process SPQs on large relations containing stochastic attributes with high variance. Besides enriching the SPaQL language to capture a broader class of risk specifications, we make two fundamental contributions towards scalable SPQ processing. First, to handle high variance, we propose risk-constraint linearization (RCL), which converts SPQs into Integer Linear Programs (ILPs) whose size is independent of the number of scenarios used. Solving these ILPs gives us feasible and near-optimal packages. Second, we propose Stochastic SketchRefine, a divide and conquer framework that breaks down a large stochastic optimization problem into subproblems involving smaller subsets of tuples. Our experiments show that, together, RCL and Stochastic SketchRefine produce high-quality packages in orders of magnitude lower runtime than the state of the art.

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arXiv2024

ChARLES: Change-Aware Recovery of Latent Evolution Semantics in Relational Data

Data-driven decision-making is at the core of many modern applications, and understanding the data is critical in supporting trust in these decisions. However, data is dynamic and evolving, just like the real-world entities it represents. Thus, an important component of understanding data is analyzing and drawing insights from the changes it undergoes. Existing methods for exploring data change list differences exhaustively, which are not interpretable by humans and lack salient insights regarding change trends. For example, an explanation that semantically summarizes changes to highlight gender disparities in performance rewards is more human-consumable than a long list of employee salary changes. We demonstrate ChARLES, a system that derives semantic summaries of changes between two snapshots of an evolving database, in an effective, concise, and interpretable way. Our key observation is that, while datasets often evolve through point and other small-batch updates, rich data features can reveal latent semantics that can intuitively summarize the changes. Under the hood, ChARLES compares database versions, infers feasible transformations by fitting multiple regression lines over different data partitions to derive change summaries, and ranks them. ChARLES allows users to customize it to obtain their preferred explanation by navigating the accuracy-interpretability tradeoff, and offers a proof of concept for reasoning about data evolution over real-world datasets.

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arXiv2023

Scaling Package Queries to a Billion Tuples via Hierarchical Partitioning and Customized Optimization

A package query returns a package - a multiset of tuples - that maximizes or minimizes a linear objective function subject to linear constraints, thereby enabling in-database decision support. Prior work has established the equivalence of package queries to Integer Linear Programs (ILPs) and developed the SketchRefine algorithm for package query processing. While this algorithm was an important first step toward supporting prescriptive analytics scalably inside a relational database, it struggles when the data size grows beyond a few hundred million tuples or when the constraints become very tight. In this paper, we present Progressive Shading, a novel algorithm for processing package queries that can scale efficiently to billions of tuples and gracefully handle tight constraints. Progressive Shading solves a sequence of optimization problems over a hierarchy of relations, each resulting from an ever-finer partitioning of the original tuples into homogeneous groups until the original relation is obtained. This strategy avoids the premature discarding of high-quality tuples that can occur with SketchRefine. Our novel partitioning scheme, Dynamic Low Variance, can handle very large relations with multiple attributes and can dynamically adapt to both concentrated and spread-out sets of attribute values, provably outperforming traditional partitioning schemes such as KD-tree. We further optimize our system by replacing our off-the-shelf optimization software with customized ILP and LP solvers, called Dual Reducer and Parallel Dual Simplex respectively, that are highly accurate and orders of magnitude faster.

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arXiv2023

Non-Invasive Fairness in Learning through the Lens of Data Drift

Machine Learning (ML) models are widely employed to drive many modern data systems. While they are undeniably powerful tools, ML models often demonstrate imbalanced performance and unfair behaviors. The root of this problem often lies in the fact that different subpopulations commonly display divergent trends: as a learning algorithm tries to identify trends in the data, it naturally favors the trends of the majority groups, leading to a model that performs poorly and unfairly for minority populations. Our goal is to improve the fairness and trustworthiness of ML models by applying only non-invasive interventions, i.e., without altering the data or the learning algorithm. We use a simple but key insight: the divergence of trends between different populations, and, consecutively, between a learned model and minority populations, is analogous to data drift, which indicates the poor conformance between parts of the data and the trained model. We explore two strategies (model-splitting and reweighing) to resolve this drift, aiming to improve the overall conformance of models to the underlying data. Both our methods introduce novel ways to employ the recently-proposed data profiling primitive of Conformance Constraints. Our experimental evaluation over 7 real-world datasets shows that both DifFair and ConFair improve the fairness of ML models. We demonstrate scenarios where DifFair has an edge, though ConFair has the greatest practical impact and outperforms other baselines. Moreover, as a model-agnostic technique, ConFair stays robust when used against different models than the ones on which the weights have been learned, which is not the case for other state of the art.

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arXiv2022

Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification

Classification, a heavily-studied data-driven machine learning task, drives an increasing number of prediction systems involving critical human decisions such as loan approval and criminal risk assessment. However, classifiers often demonstrate discriminatory behavior, especially when presented with biased data. Consequently, fairness in classification has emerged as a high-priority research area. Data management research is showing an increasing presence and interest in topics related to data and algorithmic fairness, including the topic of fair classification. The interdisciplinary efforts in fair classification, with machine learning research having the largest presence, have resulted in a large number of fairness notions and a wide range of approaches that have not been systematically evaluated and compared. In this paper, we contribute a broad analysis of 13 fair classification approaches and additional variants, over their correctness, fairness, efficiency, scalability, robustness to data errors, sensitivity to underlying ML model, data efficiency, and stability using a variety of metrics and real-world datasets. Our analysis highlights novel insights on the impact of different metrics and high-level approach characteristics on different aspects of performance. We also discuss general principles for choosing approaches suitable for different practical settings, and identify areas where data-management-centric solutions are likely to have the most impact.

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arXiv2022

Improved Approximation and Scalability for Fair Max-Min Diversification

Given an $n$-point metric space $(\mathcal{X},d)$ where each point belongs to one of $m=O(1)$ different categories or groups and a set of integers $k_1, \ldots, k_m$, the fair Max-Min diversification problem is to select $k_i$ points belonging to category $i\in [m]$, such that the minimum pairwise distance between selected points is maximized. The problem was introduced by Moumoulidou et al. [ICDT 2021] and is motivated by the need to down-sample large data sets in various applications so that the derived sample achieves a balance over diversity, i.e., the minimum distance between a pair of selected points, and fairness, i.e., ensuring enough points of each category are included. We prove the following results: 1. We first consider general metric spaces. We present a randomized polynomial time algorithm that returns a factor $2$-approximation to the diversity but only satisfies the fairness constraints in expectation. Building upon this result, we present a $6$-approximation that is guaranteed to satisfy the fairness constraints up to a factor $1-ε$ for any constant $ε$. We also present a linear time algorithm returning an $m+1$ approximation with exact fairness. The best previous result was a $3m-1$ approximation. 2. We then focus on Euclidean metrics. We first show that the problem can be solved exactly in one dimension. For constant dimensions, categories and any constant $ε>0$, we present a $1+ε$ approximation algorithm that runs in $O(nk) + 2^{O(k)}$ time where $k=k_1+\ldots+k_m$. We can improve the running time to $O(nk)+ poly(k)$ at the expense of only picking $(1-ε) k_i$ points from category $i\in [m]$. Finally, we present algorithms suitable to processing massive data sets including single-pass data stream algorithms and composable coresets for the distributed processing.

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arXiv2021

DataExposer: Exposing Disconnect between Data and Systems

As data is a central component of many modern systems, the cause of a system malfunction may reside in the data, and, specifically, particular properties of the data. For example, a health-monitoring system that is designed under the assumption that weight is reported in imperial units (lbs) will malfunction when encountering weight reported in metric units (kilograms). Similar to software debugging, which aims to find bugs in the mechanism (source code or runtime conditions), our goal is to debug the data to identify potential sources of disconnect between the assumptions about the data and the systems that operate on that data. Specifically, we seek which properties of the data cause a data-driven system to malfunction. We propose DataExposer, a framework to identify data properties, called profiles, that are the root causes of performance degradation or failure of a system that operates on the data. Such identification is necessary to repair the system and resolve the disconnect between data and system. Our technique is based on causal reasoning through interventions: when a system malfunctions for a dataset, DataExposer alters the data profiles and observes changes in the system's behavior due to the alteration. Unlike statistical observational analysis that reports mere correlations, DataExposer reports causally verified root causes, in terms of data profiles, of the system malfunction. We empirically evaluate DataExposer on three real-world and several synthetic data-driven systems that fail on datasets due to a diverse set of reasons. In all cases, DataExposer identifies the root causes precisely while requiring orders of magnitude fewer interventions than prior techniques.

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arXiv2021

Stochastic Package Queries in Probabilistic Databases

We provide methods for in-database support of decision making under uncertainty. Many important decision problems correspond to selecting a package (bag of tuples in a relational database) that jointly satisfy a set of constraints while minimizing some overall cost function; in most real-world problems, the data is uncertain. We provide methods for specifying -- via a SQL extension -- and processing stochastic package queries (SPQs), in order to solve optimization problems over uncertain data, right where the data resides. Prior work in stochastic programming uses Monte Carlo methods where the original stochastic optimization problem is approximated by a large deterministic optimization problem that incorporates many scenarios, i.e., sample realizations of the uncertain data values. For large database tables, however, a huge number of scenarios is required, leading to poor performance and, often, failure of the solver software. We therefore provide a novel SummarySearch algorithm that, instead of trying to solve a large deterministic problem, seamlessly approximates it via a sequence of smaller problems defined over carefully crafted summaries of the scenarios that accelerate convergence to a feasible and near-optimal solution. Experimental results on our prototype system show that SummarySearch can be orders of magnitude faster than prior methods at finding feasible and high-quality packages.

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arXiv2021

Conformance Constraint Discovery: Measuring Trust in Data-Driven Systems

The reliability and proper function of data-driven applications hinge on the data's continued conformance to the applications' initial design. When data deviates from this initial profile, system behavior becomes unpredictable. Data profiling techniques such as functional dependencies and denial constraints encode patterns in the data that can be used to detect deviations. But traditional methods typically focus on exact constraints and categorical attributes, and are ill-suited for tasks such as determining whether the prediction of a machine learning system can be trusted or for quantifying data drift. In this paper, we introduce data invariants, a new data-profiling primitive that models arithmetic relationships involving multiple numerical attributes within a (noisy) dataset and which complements the existing data-profiling techniques. We propose a quantitative semantics to measure the degree of violation of a data invariant, and establish that strong data invariants can be constructed from observations with low variance on the given dataset. A concrete instance of this principle gives the surprising result that low-variance components of a principal component analysis (PCA), which are usually discarded, generate better invariants than the high-variance components. We demonstrate the value of data invariants on two applications: trusted machine learning and data drift. We empirically show that data invariants can (1) reliably detect tuples on which the prediction of a machine-learned model should not be trusted, and (2) quantify data drift more accurately than the state-of-the-art methods. Additionally, we show four case studies where an intervention-centric explanation tool uses data invariants to explain causes for tuple non-conformance.

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arXiv2021

Example-Driven User Intent Discovery: Empowering Users to Cross the SQL Barrier Through Query by Example

Traditional data systems require specialized technical skills where users need to understand the data organization and write precise queries to access data. Therefore, novice users who lack technical expertise face hurdles in perusing and analyzing data. Existing tools assist in formulating queries through keyword search, query recommendation, and query auto-completion, but still require some technical expertise. An alternative method for accessing data is Query by Example (QBE), where users express their data exploration intent simply by providing examples of their intended data. We study a state-of-the-art QBE system called SQuID, and contrast it with traditional SQL querying. Our comparative user studies demonstrate that users with varying expertise are significantly more effective and efficient with SQuID than SQL. We find that SQuID eliminates the barriers in studying the database schema, formalizing task semantics, and writing syntactically correct SQL queries, and thus, substantially alleviates the need for technical expertise in data exploration.

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arXiv2020

Diverse Data Selection under Fairness Constraints

Diversity is an important principle in data selection and summarization, facility location, and recommendation systems. Our work focuses on maximizing diversity in data selection, while offering fairness guarantees. In particular, we offer the first study that augments the Max-Min diversification objective with fairness constraints. More specifically, given a universe $U$ of $n$ elements that can be partitioned into $m$ disjoint groups, we aim to retrieve a $k$-sized subset that maximizes the pairwise minimum distance within the set (diversity) and contains a pre-specified $k_i$ number of elements from each group $i$ (fairness). We show that this problem is NP-complete even in metric spaces, and we propose three novel algorithms, linear in $n$, that provide strong theoretical approximation guarantees for different values of $m$ and $k$. Finally, we extend our algorithms and analysis to the case where groups can be overlapping.

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arXiv2020

New Results for the Complexity of Resilience for Binary Conjunctive Queries with Self-Joins

The resilience of a Boolean query is the minimum number of tuples that need to be deleted from the input tables in order to make the query false. A solution to this problem immediately translates into a solution for the more widely known problem of deletion propagation with source-side effects. In this paper, we give several novel results on the hardness of the resilience problem for $\textit{binary conjunctive queries with self-joins}$ (i.e. conjunctive queries with relations of maximal arity 2) with one repeated relation. Unlike in the self-join free case, the concept of triad is not enough to fully characterize the complexity of resilience. We identify new structural properties, namely chains, confluences and permutations, which lead to various $NP$-hardness results. We also give novel involved reductions to network flow to show certain cases are in $P$. Overall, we give a dichotomy result for the restricted setting when one relation is repeated at most 2 times, and we cover many of the cases for 3. Although restricted, our results provide important insights into the problem of self-joins that we hope can help solve the general case of all conjunctive queries with self-joins in the future.

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arXiv2020

Causality-Guided Adaptive Interventional Debugging

Runtime nondeterminism is a fact of life in modern database applications. Previous research has shown that nondeterminism can cause applications to intermittently crash, become unresponsive, or experience data corruption. We propose Adaptive Interventional Debugging (AID) for debugging such intermittent failures. AID combines existing statistical debugging, causal analysis, fault injection, and group testing techniques in a novel way to (1) pinpoint the root cause of an application's intermittent failure and (2) generate an explanation of how the root cause triggers the failure. AID works by first identifying a set of runtime behaviors (called predicates) that are strongly correlated to the failure. It then utilizes temporal properties of the predicates to (over)-approximate their causal relationships. Finally, it uses fault injection to execute a sequence of interventions on the predicates and discover their true causal relationships. This enables AID to identify the true root cause and its causal relationship to the failure. We theoretically analyze how fast AID can converge to the identification. We evaluate AID with six real-world applications that intermittently fail under specific inputs. In each case, AID was able to identify the root cause and explain how the root cause triggered the failure, much faster than group testing and more precisely than statistical debugging. We also evaluate AID with many synthetically generated applications with known root causes and confirm that the benefits also hold for them.

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